We plan to host the dataset on a publicly accessible server and make it available upon request. In the meantime, please contact Dr. Maria del C. Valdés-Hernández ([email protected]) to inquire about the dataset used in this work.
- Data should have the following structure
clinical-super-mri/ data/ affine/ SR_002_NHSRI_V0_affine.mat SR_002_NHSRI_V1_affine.mat SR_005_BRIC1_V0_affine.mat ... rigid/ SR_002_NHSRI_V0_rigid.mat SR_002_NHSRI_V1_rigid.mat SR_005_BRIC1_V0_rigid.mat ... warp/ SR_002_NHSRI_V0.mat SR_002_NHSRI_V1.mat SR_005_BRIC1_V0.mat ...
- Here we provide a simple Python script
mat2npy.py
to convert.mat
files to.npy
as loading.npy
files is much faster in Python. - The follow command convert all
.mat
scans inaffine/
to.npy
files and store inaffine/npy
python mat2npy.py --input_dir affine --output_dir affine/npy
- Note that our data reader still support reading directly from
.mat
files though the training speed might be bottlenecked by reading.mat
files.