Subcortical segmentation remains challenging despite its important applications in quantitative structural analysis of brain MRI scans. The most accurate method, manual segmentation, is highly labor intensive, so automated tools like FreeSurfer have been adopted to handle this task. However, these traditional pipelines are slow and inefficient for processing large datasets. In this study, we propose TABSurfer, a novel 3D patch-based CNN-Transformer hybrid deep learning model designed for superior subcortical segmentation compared to existing state-of-the-art tools. To evaluate, we first demonstrate TABSurfer's consistent performance across various T1w MRI datasets with significantly shorter processing times compared to FreeSurfer. Then, we validate against manual segmentations, where TABSurfer outperforms FreeSurfer based on the manual ground truth. In each test, we also establish TABSurfer's advantage over a leading deep learning benchmark, FastSurferVINN. Together, these studies highlight TABSurfer's utility as a powerful tool for fully automated subcortical segmentation with high fidelity.

翻译：皮层下分割尽管在脑部MRI扫描的定量结构分析中具有重要应用，但仍然具有挑战性。最准确的方法——手动分割，劳动强度极高，因此像FreeSurfer这样的自动化工具已被广泛采用。然而，这些传统流程在处理大型数据集时速度慢且效率低下。在本研究中，我们提出了TABSurfer——一种新颖的基于3D补丁的CNN-Transformer混合深度学习模型，旨在实现优于现有最先进工具的皮层下分割性能。为进行评估，我们首先展示了TABSurfer在各种T1w MRI数据集上的一致性能，且处理时间显著短于FreeSurfer。然后，我们基于手动标注的真实值进行验证，结果表明TABSurfer在分割准确性上优于FreeSurfer。在每项测试中，我们还确立了TABSurfer相对于领先的深度学习基准方法FastSurferVINN的优势。这些研究共同凸显了TABSurfer作为一种高保真度、全自动皮层下分割工具的实用性。