Skip to content

Latest commit

 

History

History
14 lines (13 loc) · 2.03 KB

File metadata and controls

14 lines (13 loc) · 2.03 KB
  1. Install FreeSurfer from https://surfer.nmr.mgh.harvard.edu/fswiki/DownloadAndInstall
  2. export FREESURFER_HOME=/your_freesurfer_directory
  3. source $FREESURFER_HOME/SetUpFreeSurfer.sh
  4. export SUBJECTS_DIR=/dataset_directory
  5. recon-all -parallel -i dataset_directory/img_name.nii -autorecon1 -subjid img_name -> This step does motion correction, skull stripping, affine transform comuptation, and intensity normalization.
  6. mri_convert dataset_directory/img_name/mri/brainmask.mgz dataset_directory/img_name/mri/brainmask.nii.gz -> This step converts the preprocessed image from .mgz into .nii format.
  7. mri_convert dataset_directory/img_name/mri/brainmask.mgz --apply_transform dataset_directory/img_name/mri/transforms/talairach.xfm -o dataset_directory/img_name/mri/brainmask_align.mgz -> This step does affine tranform to Talairach space.
  8. mri_convert dataset_directory/img_name/mri/brainmask_align.mgz dataset_directory/img_name/mri/brainmask_align.nii.gz -> This step converts the transformed image from .mgz into .nii format.
  9. recon-all -parallel -s dataset_directory/img_name.nii -subcortseg -subjid img_name -> This step does subcortical segmentation.
  10. mri_convert dataset_directory/img_name/mri/aseg.auto.mgz dataset_directory/img_name/mri/aseg.nii.gz -> This step converts label image from .mgz into .nii format.
  11. mri_convert -rt nearest dataset_directory/img_name/mri/aseg.auto.mgz --apply_transform dataset_directory/img_name/mri/transforms/talairach.xfm -o dataset_directory/img_name/mri/aseg_align.mgz -> This step does affine tranform to Talairach space using nearest neighbor interpolation for label image.
  12. mri_convert dataset_directory/img_name/mri/aseg_align.mgz dataset_directory/img_name/mri/aseg_align.nii.gz -> This step converts the transformed label image from .mgz into .nii format.

Note that these steps may take up to 12-24 hours per image base on our experience. Therefore running these commands in parallel on a server or a cluster is recommended.