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Abstract #4817

Improving Across-Dataset Brain Tissue Segmentation Using Transformer

Vishwanatha Mitnala Rao1, Zihan Wan2, David Ma1, Ye Tian1, and Jia Guo3
1Department of Biomedical Engineering, Columbia University, New York, NY, United States, 2Department of Applied Mathematics, Columbia University, New York, NY, United States, 3Department of Psychiatry, Columbia University, New York, NY, United States


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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