The dataset was provided by Andrew Hoopes and Adrian V. Dalca in support of the HyperMorph research. If you utilize this dataset, please acknowledge the original work by citing the associated HyperMorph paper and adhere to the OASIS Data Use Agreement.
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HyperMorph: Amortized Hyperparameter Learning for Image Registration.
Hoopes A, Hoffmann M, Fischl B, Guttag J, Dalca AV. IPMI 2021. -
Open Access Series of Imaging Studies (OASIS): Cross-Sectional MRI Data in Young, Middle Aged, Nondemented, and Demented Older Adults.
Marcus DS, Wang TH, Parker J, Csernansky JG, Morris JC, Buckner RL.
Journal of Cognitive Neuroscience, 19, 1498-1507.
Inspired by TransMorph, this dataset has been converted to .pkl format and is available for download via this Dropbox link: oasis_pkl. To facilitate the use of our code with this dataset, a complete project setup is included in the shared folder. Once the necessary Python packages are installed, you can directly execute the project using the following command:
python train_brainreg.py -d oasis_pkl -m brainTextSCFComplex -bs 1 --epochs 501 --reg_w 0.1 start_channel=64 scp_dim=2048 diff_int=0 clip_backbone=vit
To use the pretrained mode, download the complete project setup, run the script with the following command in folder ./src to get the npz files:
python test_brainreg.py -d oasis_pkl -m brainTextSCFComplex -bs 1 --is_submit 1 --load_ckpt ./../../../checkpoint/oasis_9002_64_2048_0_vit.pth start_channel=64 scp_dim=2048 diff_int=0 clip_backbone=vit
--is_submit: Whether to create npz files for submission to the challenge.--load_ckpt: The type of the checkpoint to load, 'last' is from the latest checkpoint, 'best' is from the checkpoint with highest validation score, and a path such as './../../../checkpoint/oasis_9002_64_2048_0_vit.pth' directing to the checkpoint.
The npz files will be saved at ./textSCF/src/logs/oasis_pkl/brainTextSCFComplex/ where textSCF is the root of the code repository.
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