AUTHOR=Rao Vishwanatha M. , Wan Zihan , Arabshahi Soroush , Ma David J. , Lee Pin-Yu , Tian Ye , Zhang Xuzhe , Laine Andrew F. , Guo Jia TITLE=Improving across-dataset brain tissue segmentation for MRI imaging using transformer JOURNAL=Frontiers in Neuroimaging VOLUME=1 YEAR=2022 URL=https://www.frontiersin.org/journals/neuroimaging/articles/10.3389/fnimg.2022.1023481 DOI=10.3389/fnimg.2022.1023481 ISSN=2813-1193 ABSTRACT=

Brain tissue segmentation has demonstrated great utility in quantifying MRI data by serving as a precursor to further post-processing analysis. However, manual segmentation is highly labor-intensive, and automated approaches, including convolutional neural networks (CNNs), have struggled to generalize well due to properties inherent to MRI acquisition, leaving a great need for an effective segmentation tool. This study introduces a novel CNN-Transformer hybrid architecture designed to improve brain tissue segmentation by taking advantage of the increased performance and generality conferred by Transformers for 3D medical image segmentation tasks. We first demonstrate the superior performance of our model on various T1w MRI datasets. Then, we rigorously validate our model's generality applied across four multi-site T1w MRI datasets, covering different vendors, field strengths, scan parameters, and neuropsychiatric conditions. Finally, we highlight the reliability of our model on test-retest scans taken in different time points. In all situations, our model achieved the greatest generality and reliability compared to the benchmarks. As such, our 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.