interfaces.freesurfer.longitudinal

FuseSegmentations

Link to code

Wraps the executable command mri_fuse_segmentations.

fuse segmentations together from multiple timepoints

Examples

>>> from nipype.interfaces.freesurfer import FuseSegmentations
>>> fuse = FuseSegmentations()
>>> fuse.inputs.subject_id = 'tp.long.A.template'
>>> fuse.inputs.timepoints = ['tp1', 'tp2']
>>> fuse.inputs.out_file = 'aseg.fused.mgz'
>>> fuse.inputs.in_segmentations = ['aseg.mgz', 'aseg.mgz']
>>> fuse.inputs.in_segmentations_noCC = ['aseg.mgz', 'aseg.mgz']
>>> fuse.inputs.in_norms = ['norm.mgz', 'norm.mgz', 'norm.mgz']
>>> fuse.cmdline
'mri_fuse_segmentations -n norm.mgz -a aseg.mgz -c aseg.mgz tp.long.A.template tp1 tp2'

Inputs:

[Mandatory]
timepoints: (a list of items which are a string)
        subject_ids or timepoints to be processed
        argument: ``%s``, position: -2
out_file: (a file name)
        output fused segmentation file
in_segmentations: (a list of items which are an existing file name)
        name of aseg file to use (default: aseg.mgz) must include the aseg
        files for all the given timepoints
        argument: ``-a %s``
in_segmentations_noCC: (a list of items which are an existing file
          name)
        name of aseg file w/o CC labels (default: aseg.auto_noCCseg.mgz)
        must include the corresponding file for all the given timepoints
        argument: ``-c %s``
in_norms: (a list of items which are an existing file name)
        -n <filename> - name of norm file to use (default: norm.mgs) must
        include the corresponding norm file for all given timepoints as well
        as for the current subject
        argument: ``-n %s``

[Optional]
subject_id: (a string)
        subject_id being processed
        argument: ``%s``, position: -3
subjects_dir: (an existing directory name)
        subjects directory
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

out_file: (a file name)
        output fused segmentation file

RobustTemplate

Link to code

Wraps the executable command mri_robust_template.

construct an unbiased robust template for longitudinal volumes

Examples

>>> from nipype.interfaces.freesurfer import RobustTemplate
>>> template = RobustTemplate()
>>> template.inputs.in_files = ['structural.nii', 'functional.nii']
>>> template.inputs.auto_detect_sensitivity = True
>>> template.inputs.average_metric = 'mean'
>>> template.inputs.initial_timepoint = 1
>>> template.inputs.fixed_timepoint = True
>>> template.inputs.no_iteration = True
>>> template.inputs.subsample_threshold = 200
>>> template.cmdline  #doctest:
'mri_robust_template --satit --average 0 --fixtp --mov structural.nii functional.nii --inittp 1 --noit --template mri_robust_template_out.mgz --subsample 200'
>>> template.inputs.out_file = 'T1.nii'
>>> template.cmdline  #doctest:
'mri_robust_template --satit --average 0 --fixtp --mov structural.nii functional.nii --inittp 1 --noit --template T1.nii --subsample 200'

>>> template.inputs.transform_outputs = ['structural.lta',
...                                      'functional.lta']
>>> template.inputs.scaled_intensity_outputs = ['structural-iscale.txt',
...                                             'functional-iscale.txt']
>>> template.cmdline    
'mri_robust_template --satit --average 0 --fixtp --mov structural.nii functional.nii --inittp 1 --noit --template T1.nii --iscaleout .../structural-iscale.txt .../functional-iscale.txt --subsample 200 --lta .../structural.lta .../functional.lta'

>>> template.inputs.transform_outputs = True
>>> template.inputs.scaled_intensity_outputs = True
>>> template.cmdline    
'mri_robust_template --satit --average 0 --fixtp --mov structural.nii functional.nii --inittp 1 --noit --template T1.nii --iscaleout .../is1.txt .../is2.txt --subsample 200 --lta .../tp1.lta .../tp2.lta'

>>> template.run()  

References

[https://surfer.nmr.mgh.harvard.edu/fswiki/mri_robust_template]

Inputs:

[Mandatory]
in_files: (a list of items which are an existing file name)
        input movable volumes to be aligned to common mean/median template
        argument: ``--mov %s``
out_file: (a file name, nipype default value:
          mri_robust_template_out.mgz)
        output template volume (final mean/median image)
        argument: ``--template %s``
auto_detect_sensitivity: (a boolean)
        auto-detect good sensitivity (recommended for head or full brain
        scans)
        argument: ``--satit``
        mutually_exclusive: outlier_sensitivity
outlier_sensitivity: (a float)
        set outlier sensitivity manually (e.g. "--sat 4.685" ). Higher
        values mean less sensitivity.
        argument: ``--sat %.4f``
        mutually_exclusive: auto_detect_sensitivity

[Optional]
transform_outputs: (a list of items which are a file name or a
          boolean)
        output xforms to template (for each input)
        argument: ``--lta %s``
intensity_scaling: (a boolean)
        allow also intensity scaling (default off)
        argument: ``--iscale``
scaled_intensity_outputs: (a list of items which are a file name or a
          boolean)
        final intensity scales (will activate --iscale)
        argument: ``--iscaleout %s``
subsample_threshold: (an integer (int or long))
        subsample if dim > # on all axes (default no subs.)
        argument: ``--subsample %d``
average_metric: ('median' or 'mean')
        construct template from: 0 Mean, 1 Median (default)
        argument: ``--average %d``
initial_timepoint: (an integer (int or long))
        use TP# for spacial init (default random), 0: no init
        argument: ``--inittp %d``
fixed_timepoint: (a boolean)
        map everthing to init TP# (init TP is not resampled)
        argument: ``--fixtp``
no_iteration: (a boolean)
        do not iterate, just create first template
        argument: ``--noit``
initial_transforms: (a list of items which are an existing file name)
        use initial transforms (lta) on source
        argument: ``--ixforms %s``
in_intensity_scales: (a list of items which are an existing file
          name)
        use initial intensity scales
        argument: ``--iscalein %s``
num_threads: (an integer (int or long))
        allows for specifying more threads
subjects_dir: (an existing directory name)
        subjects directory
args: (a unicode string)
        Additional parameters to the command
        argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
          of class 'str' and with values which are a bytes or None or a
          value of class 'str', nipype default value: {})
        Environment variables

Outputs:

out_file: (an existing file name)
        output template volume (final mean/median image)
transform_outputs: (a list of items which are an existing file name)
        output xform files from moving to template
scaled_intensity_outputs: (a list of items which are an existing file
          name)
        output final intensity scales