LONG: long_fastsurfer.sh

Note

Please also see the documentation on Longitudinal Processing.

Usage help text

$ ./long_fastsurfer.sh --help
Setting ENV variable FASTSURFER_HOME to script directory /home/runner/work/FastSurfer/FastSurfer/src. 
Change via environment to location of your choice if this is undesired (export FASTSURFER_HOME=/dir/to/FastSurfer)

Usage: long_fastsurfer.sh --tid <tid> --t1s <T1_1> <T1_2> .. --tpids <tID1> <tID2> .. [OPTIONS]

long_fastsurfer.sh takes a list of T1 full head image and sequentially creates:
     (i)   a template subject directory 
     (ii)  directories for each processed time point in template space,
           here you find the final longitudinal results

FLAGS:

  --tid <templateID>        ID for subject template/base directory inside
                              $SUBJECTS_DIR to be created"
  --t1s <T1_1> <T1_2> ..    T1 full head inputs for each time point (do not need
                              to be bias corrected). Requires ABSOLUTE paths!
  --tpids <tID1> >tID2> ..  IDs for future time points directories inside
                              $SUBJECTS_DIR to be created later (during --long)
  --sd  <subjects_dir>      Output directory $SUBJECTS_DIR (or pass via env var)
  --py <python_cmd>         Command for python, used in both pipelines.
                              Default: "python3.10 -s"
                              (-s: do no search for packages in home directory)
  -h --help                 Print Help

Parallelization options:
  All of the following options will activate parallel processing of the base and the longitudinal time-point images
  where possible. Additionally, the number of different processes for segmentation and surface reconstructionis set.
  --parallel <n>|max        See above, sets the size of the processing pool for segmentation and surface reconstruction
  --parallel_seg <n>|max    See above, only sets the size of the processing pool for segmentation (default: 1)
  --parallel_surf <n>|max   See above, only sets the size of the processing pool for surface reconstruction (default: 1)


With the exception of --t1, --t2, --sid, --seg_only and --surf_only, all
run_fastsurfer.sh options are supported, see 'run_fastsurfer.sh --help'.


REFERENCES:

If you use this for research publications, please cite:

For FastSurfer (both):
Henschel L, Conjeti S, Estrada S, Diers K, Fischl B, Reuter M, FastSurfer - A
 fast and accurate deep learning based neuroimaging pipeline, NeuroImage 219
 (2020), 117012. https://doi.org/10.1016/j.neuroimage.2020.117012

Henschel L*, Kuegler D*, Reuter M. (*co-first). FastSurferVINN: Building
 Resolution-Independence into Deep Learning Segmentation Methods - A Solution
 for HighRes Brain MRI. NeuroImage 251 (2022), 118933. 
 http://dx.doi.org/10.1016/j.neuroimage.2022.118933

And for longitudinal processing:
Reuter M, Schmansky NJ, Rosas HD, Fischl B. Within-subject template estimation
 for unbiased longitudinal image analysis, NeuroImage 61:4 (2012).
 https://doi.org/10.1016/j.neuroimage.2012.02.084

For cerebellum sub-segmentation:
Faber J*, Kuegler D*, Bahrami E*, et al. (*co-first). CerebNet: A fast and
 reliable deep-learning pipeline for detailed cerebellum sub-segmentation.
 NeuroImage 264 (2022), 119703.
 https://doi.org/10.1016/j.neuroimage.2022.119703

For hypothalamus sub-segemntation:
Estrada S, Kuegler D, Bahrami E, Xu P, Mousa D, Breteler MMB, Aziz NA, Reuter M.
 FastSurfer-HypVINN: Automated sub-segmentation of the hypothalamus and adjacent
 structures on high-resolutional brain MRI. Imaging Neuroscience 2023; 1 1–32.
 https://doi.org/10.1162/imag_a_00034