Accurate identification of brain tissue and cerebrospinal fluid (CSF) in a whole-head MRI is a critical first step in many neuroimaging studies. Automating this procedure can eliminate intra- and interrater variance and greatly increase throughput for a labor-intensive step. Many available procedures perform differently across anatomy and under different acquisition protocols. We developed the Brain Extraction Meta-Algorithm (BEMA) to address these concerns. It executes many extraction algorithms and a registration procedure in parallel to combine the results in an intelligent fashion and obtain improved results over any of the individual algorithms. Using an atlas space, BEMA performs a voxelwise analysis of training data to determine the optimal Boolean combination of extraction algorithms to produce the most accurate result for a given voxel. This allows the provided extractors to be used differentially across anatomy, increasing both the accuracy and robustness of the procedure. We tested BEMA using modified forms of BrainSuite's Brain Surface Extractor (BSE), FSL's Brain Extraction Tool (BET), AFNI's 3dIntracranial, and FreeSurfer's MRI Watershed as well as FSL's FLIRT for the registration procedure. Training was performed on T1-weighted scans of 136 subjects from five separate data sets with different acquisition parameters on separate scanners. Testing was performed on 135 separate subjects from the same data sets. BEMA outperformed the individual algorithms, as well as interrater results from a subset of the scans, when compared for the mean Dice coefficient, a rating of the similarity of output masks to the manually defined gold standards.
Bibliographical noteFunding Information:
This work was generously supported by grants from the National Institute of Mental Health (1 P20 MH65166 and 5 P01 MH52176) and the National Center for Research Resources (2 P41 RR13642 and 2 M01 RR00865), with a supplement for the Biomedical Informatics Research Network (2 P41 RR13642) ( http://www.nbirn.net/ ). DER is supported, in part, by an ARCS Foundation scholarship and a National Institutes of General Medical Sciences Medical Scientist Training Program grant (GM08042). The authors wish to thank Drs. Robert Bilder, John Mazziotta, and Tonmoy Sharma for providing the LIJMC, ICBM, and IPDH data sets, respectively. The authors also wish to thank the members of the Laboratory of Neuro Imaging for their help and support.
- Automated processing
- Brain extraction