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.

翻译：脑组织分割在通过基于体素的形态学分析量化MRI数据及突出脑内与多种状况相关的细微结构变化方面展现出巨大价值。然而，手动分割劳动强度极高，而由于MRI采集固有的特性，自动化方法面临诸多挑战，因此亟需一种有效的分割工具。尽管深度卷积神经网络（CNN）近期在脑组织分割中取得了成功，但许多此类解决方案难以很好地泛化到新数据集，而这对于可靠的解决方案至关重要。Transformer凭借其捕捉输入中长距离关系的能力（而CNN的局部感受野在此方面存在局限），在自然图像分割中已展现出成功，并近期被应用于三维医学图像分割任务。本研究提出了一种专为脑组织分割设计的新型CNN-Transformer混合架构。我们在四个多站点T1加权MRI数据集上验证了模型的性能，这些数据集涵盖不同供应商、场强、扫描参数、时间点及神经精神状况。在所有情况下，我们的模型均实现了最佳泛化性和可靠性。该方法本质上具有鲁棒性，可成为脑相关T1加权MRI研究的有价值工具。TABS网络的代码可在以下网址获取：https://github.com/raovish6/TABS。