SST-DUNet: Smart Swin Transformer and Dense UNet for automated preclinical fMRI skull stripping
The fMRI results using SST-DUNet closely align with those from manual skull stripping for both seed-based and independent component analyses, indicating that SST-DUNet can effectively substitute manual brain extraction in rat fMRI analysis.
Authors
- Arnold Chang
- Dan Madularu (ORCID: https://orcid.org/0000-0003-0040-3187)
- Praveen Kulkarni (ORCID: https://orcid.org/0000-0002-8876-4544)
- Chris Joslin (ORCID: https://orcid.org/0000-0002-6728-2722)
- Craig F. Ferris (ORCID: https://orcid.org/0000-0001-9744-5214)
- Md Taufiq Nasseef (ORCID: https://orcid.org/0000-0002-4594-8458)
- Sima Soltanpour (ORCID: https://orcid.org/0000-0002-2131-8902)
- Rachel Utama
Institutions
- Northeastern University (US)
- Prince Sattam Bin Abdulaziz University (SA)
- Carleton University (CA)
Publication Details
- Journal
- Journal of Neuroscience Methods
- Published
- 2025-08-09
- DOI
- https://doi.org/10.1016/j.jneumeth.2025.110545
- Citations
- 2
- Primary Topic
- Advanced MRI Techniques and Applications
- Type
- article
- Field-Weighted Citation Impact
- 2.72
Funders
- Mitacs