  Brain tissue segmentation has demonstrated great utility in quantifying MRI
data through Voxel-Based Morphometry and highlighting subtle structural changes
associated with various conditions within the brain. However, manual
segmentation is highly labor-intensive, and automated approaches have struggled
due to properties inherent to MRI acquisition, leaving a great need for an
effective segmentation tool. Despite the recent success of deep convolutional
neural networks (CNNs) for brain tissue segmentation, many such solutions do
not generalize well to new datasets, which is critical for a reliable solution.
Transformers have demonstrated success in natural image segmentation and have
recently been applied to 3D medical image segmentation tasks due to their
ability to capture long-distance relationships in the input where the local
receptive fields of CNNs struggle. This study introduces a novel
CNN-Transformer hybrid architecture designed for brain tissue segmentation. We
validate our model's performance across four multi-site T1w MRI datasets,
covering different vendors, field strengths, scan parameters, time points, and
neuropsychiatric conditions. In all situations, our model achieved the greatest
generality and reliability. Out method is inherently robust and can serve as a
valuable tool for brain-related T1w MRI studies. The code for the TABS network
is available at: https://github.com/raovish6/TABS.
