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* Zöllei, L, Iglesias, J.E., Ou, Y. Grant, P.E., Fischl, B.: Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years; (accepted) NeuroImage 2020    * Zöllei, L, Iglesias, J.E., Ou, Y. Grant, P.E., Fischl, B.: Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years; (accepted) NeuroImage 2020

DOWNLOAD

Code: Click here to request.

  • We have an experimental docker release image as well, so if you are interested in that one, please, make sure to indicate that! (Note, we cannot download FSL in the docker container because their new install process requires signing up or using an FSL license, so you will have to install it on your end.)

Cite:

  • Zöllei, L, Iglesias, J.E., Ou, Y. Grant, P.E., Fischl, B.: Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years; (accepted) NeuroImage 2020

  • Zöllei, L, Iglesias, J.E., Ou, Y. Grant, P.E., Fischl, B.: Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years; arXiv:2001.03091 https://arxiv.org/abs/2001.03091

  • Zöllei et al. FreeSurfer image processing pipeline for infant clinical MRI images. Human Brain Mapping, Vancouver, Canada, 2017.

  • de Macedo Rodrigues, K., et al., A FreeSurfer-compliant consistent manual segmentation of infant brains spanning the 0–2 year age range. Front. Hum. Neurosci., 2015. 9(21).

** Updates 02/10/2020:

a new version of the infant pipeline has been created for your use. See below for details about the new features.

  • In this release, we are not relying on the PICASSO skullstripping code any more. The new skullstripping tool is fully integrated into the pipeline so no new toolbox needs to be downloaded.
  • Instead of DRAMMS, the pipeline now used niftyreg (https://sourceforge.net/p/niftyreg), by default, for nonlinear registration. This gets shipped with the pipeline. If you would like to use DRAMMS, there is a flag to enable that, but you will have to install DRAMMS on your own first.

  • The pipeline was packaged with the dev version of the FreeSurfer libraries.

  • The pipeline relies on python 3.6 (and this is shipped with the release). Tensorflow is already enabled.
  • Just like previously, you will still need install FSL [https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation].

EXECUTION

The most basic command line that you can run in order to execute the pipeline is the following.

setenv SUBJECTS_DIR your_data_path      ** OR **    export SUBJECTS_DIR=your_data_path

infant_recon_all --s SUBJ --age age_in_months

You can run this release from both tcsh or bash.

Just like before, the script will expect the input file to be located at $SUBJECTS_DIR/$subj/mprage.nii.gz or $SUBJECTS_DIR/$subj/mprage.mgz by default. If the --masked flag is used, we recommend also running intensity normalization. One example would be using

mri_nu_correct.mni --i $infile --o $nufile --n 2


** Below are Notes from the original release **

Current skullstripping code: PICASSO -- need to get it independently, from NITRC. With questions related to this tool, contact Dr Ou ( yangming.ou@childrens.harvard.edu ).

EXECUTION

source set_babydev_packages.csh

setenv SUBJECTS_DIR your_data_path

infant_recon_all --s SUBJ --age age_in_months

Note, run everything from tcsh. Bash scripts are not yet ready for this distribution.

Before execution, you will need install FSL [https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FslInstallation] and dramms [www.nitrc.org].

The script will expect the input file to be located at $SUBJECTS_DIR/$subj/mprage.nii.gz or $SUBJECTS_DIR/$subj/mprage.mgz by default. If the --masked flag is used, we recommend also running intensity normalization using

mri_nu_correct.mni --i $infile --o $nufile --n 2

Other useful flags:

--masked (using a skullstripped input)

--outdir (redirecting output)

--kneigh (number of training examples to use)

See infant_recon_all --help for more information

NOTES Currently, aparc is provided for sake of completeness, but has not been fully validated. It is a projected version of the adult atlas, so it does not originate from the infant training data set!

infantFS (last edited 2020-05-20 08:57:27 by LillaZollei)