Keywords: Analysis/Processing, Neuro
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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords