TABSurfer: a Hybrid Deep Learning Architecture for Subcortical
  Segmentation
Aaron Cao Vishwanatha M. Rao Kejia Liu Xinru Liu Andrew F. Laine Jia Guo
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.