3D Hybrid Deep Learning Solution for Subcortical Segmentation

Motivation: For subcortical brain segmentation, the most widely accepted tools like FreeSurfer are slow and inefficient for large datasets, while faster methods often sacrifice accuracy and reliability. Goal(s): In this study, we propose a novel deep learning based alternative and achieve consistent state-of-the-art performance within reasonable processing times. Approach: Our model, TABSurfer, utilizes a 3D patch-based approach with a hybrid CNN-Transformer architecture. Results: We evaluated TABSurfer against FreeSurfer ground truths across various T1w MRI datasets, consistently demonstrating strong performance over a leading deep learning benchmark, FastSurferVINN. Then, we validated TABSurfer on a manual reference, outperforming both FreeSurfer and FastSurferVINN based on the gold standard. Impact: Our proposed deep learning model, TABSurfer, demonstrated state-of-the-art subcortical segmentation performance and utility. TABSurfer displayed reliability across numerous datasets and outperformed well established traditional and deep learning tools in FreeSurfer and FastSurferVINN.