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Brain Tissue Segmentation in MRI by Supervised Classification and Regularization

Segmentation and volumetry of brain tissues from MRI has interesting clinical and neuroscience applications. We report about a fully automatic brain segmentation method, which is based on voxel-wise feature extraction and tissue classification using decision forests with subsequent spatial regularization in a conditional random field context. Evaluation has been performed on two different image datasets, namely the IBSR and MRBrainS, using different similarity metrics. The average Dice coefficients for cerebrospinal fluid, gray matter and white matter range from 0.69 to 0.86. Computation time is between 2 and 3 minutes, with 1 additional minute required for skull-stripping. The segmentation tool is publicly available from our website.