Despite achieving compelling performance, many deep learning automated brain tissue segmentation solutions struggle to generalize to new datasets due to properties inherent to MRI scans. We propose TABS, a new transformer-based deep learning architecture that achieves state-of-the-art-performance, generalization, and consistency. We tested TABS on three datasets of differing field strands and acquisition parameters. TABS outperformed RAUnet on our performance testing and remained consistent across test-retest repeated scans from a separate dataset. Moreover, TABS achieved impressive generality performance and even improved in performance across datasets. We believe TABS represents a generalized and accurate brain tissue segmentation alternative.
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