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interfaces.freesurfer.model

Binarize

Code: http://github.com/nipy/nipype/blob/master/nipype/interfaces/freesurfer/model.py#L401

Wraps command mri_binarize

Use FreeSurfer mri_binarize to threshold an input volume

Examples

>>> binvol = Binarize(in_file='structural.nii', min=10, binary_file='foo_out.nii')
>>> binvol.cmdline
'mri_binarize --o foo_out.nii --i structural.nii --min 10.000000'

Inputs:

[Mandatory]
in_file: (an existing file name)
        input volume

[Optional]
abs: (a boolean)
        take abs of invol first (ie, make unsigned)
args: (a string)
        Additional parameters to the command
bin_col_num: (a boolean)
        set binarized voxel value to its column number
bin_val: (an integer)
        set vox within thresh to val (default is 1)
bin_val_not: (an integer)
        set vox outside range to val (default is 0)
binary_file: (a file name)
        binary output volume
count_file: (a boolean or a file name)
        save number of hits in ascii file (hits, ntotvox, pct)
dilate: (an integer)
        niters: dilate binarization in 3D
environ: (a dictionary with keys which are a value of type 'str' and with values which
         are a value of type 'str', nipype default value: {})
        Environment variables
erode: (an integer)
        nerode: erode binarization in 3D (after any dilation)
erode2d: (an integer)
        nerode2d: erode binarization in 2D (after any 3D erosion)
frame_no: (an integer)
        use 0-based frame of input (default is 0)
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run
invert: (a boolean)
        set binval=0, binvalnot=1
mask_file: (an existing file name)
        must be within mask
mask_thresh: (a float)
        set thresh for mask
match: (a list of items which are an integer)
        match instead of threshold
max: (a float)
        max thresh
merge_file: (an existing file name)
        merge with mergevol
min: (a float)
        min thresh
out_type: ('nii' or 'nii.gz' or 'mgz')
        output file type
rmax: (a float)
        compute max based on rmax*globalmean
rmin: (a float)
        compute min based on rmin*globalmean
subjects_dir: (an existing directory name)
        subjects directory
ventricles: (a boolean)
        set match vals those for aseg ventricles+choroid (not 4th)
wm: (a boolean)
        set match vals to 2 and 41 (aseg for cerebral WM)
wm_ven_csf: (a boolean)
        WM and ventricular CSF, including choroid (not 4th)
zero_edges: (a boolean)
        zero the edge voxels
zero_slice_edge: (a boolean)
        zero the edge slice voxels

Outputs:

binary_file: (an existing file name)
        binarized output volume
count_file: (a file name)
        ascii file containing number of hits

Concatenate

Code: http://github.com/nipy/nipype/blob/master/nipype/interfaces/freesurfer/model.py#L504

Wraps command mri_concat

Use Freesurfer mri_concat to combine several input volumes into one output volume. Can concatenate by frames, or compute a variety of statistics on the input volumes.

Examples

Combine two input volumes into one volume with two frames

>>> concat = Concatenate()
>>> concat.inputs.in_files = ['cont1.nii', 'cont2.nii']
>>> concat.inputs.concatenated_file = 'bar.nii'
>>> concat.cmdline
'mri_concat --o bar.nii --i cont1.nii --i cont2.nii'

Inputs:

[Mandatory]
in_files: (an existing file name)
        Individual volumes to be concatenated

[Optional]
add_val: (a float)
        Add some amount to the input volume
args: (a string)
        Additional parameters to the command
combine: (a boolean)
        Combine non-zero values into single frame volume
concatenated_file: (a file name)
        Output volume
environ: (a dictionary with keys which are a value of type 'str' and with values which
         are a value of type 'str', nipype default value: {})
        Environment variables
gmean: (an integer)
        create matrix to average Ng groups, Nper=Ntot/Ng
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run
keep_dtype: (a boolean)
        Keep voxelwise precision type (default is float
mask_file: (an existing file name)
        Mask input with a volume
max_bonfcor: (a boolean)
        Compute max and bonferroni correct (assumes -log10(ps))
max_index: (a boolean)
        Compute the index of max voxel in concatenated volumes
mean_div_n: (a boolean)
        compute mean/nframes (good for var)
multiply_by: (a float)
        Multiply input volume by some amount
multiply_matrix_file: (an existing file name)
        Multiply input by an ascii matrix in file
paired_stats: ('sum' or 'avg' or 'diff' or 'diff-norm' or 'diff-norm1' or 'diff-norm2')
        Compute paired sum, avg, or diff
sign: ('abs' or 'pos' or 'neg')
        Take only pos or neg voxles from input, or take abs
sort: (a boolean)
        Sort each voxel by ascending frame value
stats: ('sum' or 'var' or 'std' or 'max' or 'min' or 'mean')
        Compute the sum, var, std, max, min or mean of the input volumes
subjects_dir: (an existing directory name)
        subjects directory
vote: (a boolean)
        Most frequent value at each voxel and fraction of occurances

Outputs:

concatenated_file: (an existing file name)
        Path/name of the output volume

GLMFit

Code: http://github.com/nipy/nipype/blob/master/nipype/interfaces/freesurfer/model.py#L254

Wraps command mri_glmfit

Use FreeSurfer’s mri_glmfit to specify and estimate a general linear model.

Examples

>>> glmfit = GLMFit()
>>> glmfit.inputs.in_file = 'functional.nii'
>>> glmfit.inputs.one_sample = True
>>> glmfit.cmdline == 'mri_glmfit --glmdir %s --y functional.nii --osgm'%os.getcwd()
True

Inputs:

[Mandatory]
in_file: (a file name)
        input 4D file

[Optional]
allow_ill_cond: (a boolean)
        allow ill-conditioned design matrices
allow_repeated_subjects: (a boolean)
        allow subject names to repeat in the fsgd file (must appear before --fsgd
args: (a string)
        Additional parameters to the command
calc_AR1: (a boolean)
        compute and save temporal AR1 of residual
check_opts: (a boolean)
        don't run anything, just check options and exit
compute_log_y: (a boolean)
        compute natural log of y prior to analysis
contrast: (an existing file name)
        contrast file
cortex: (a boolean)
        use subjects ?h.cortex.label as label
        mutually_exclusive: label_file
debug: (a boolean)
        turn on debugging
design: (an existing file name)
        design matrix file
        mutually_exclusive: fsgd, design, one_sample
diag: (an integer)
        Gdiag_no : set diagnositc level
diag_cluster: (a boolean)
        save sig volume and exit from first sim loop
environ: (a dictionary with keys which are a value of type 'str' and with values which
         are a value of type 'str', nipype default value: {})
        Environment variables
fixed_fx_dof: (an integer)
        dof for fixed effects analysis
        mutually_exclusive: fixed_fx_dof_file
fixed_fx_dof_file: (a file name)
        text file with dof for fixed effects analysis
        mutually_exclusive: fixed_fx_dof
fixed_fx_var: (an existing file name)
        for fixed effects analysis
force_perm: (a boolean)
        force perumtation test, even when design matrix is not orthog
fsgd: (a tuple of the form: (an existing file name, 'doss' or 'dods'))
        freesurfer descriptor file
        mutually_exclusive: fsgd, design, one_sample
fwhm: (a float)
        smooth input by fwhm
glm_dir: (a string)
        save outputs to dir
hemi: ('lh' or 'rh')
        surface hemisphere
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run
invert_mask: (a boolean)
        invert mask
label_file: (an existing file name)
        use label as mask, surfaces only
        mutually_exclusive: cortex
mask_file: (an existing file name)
        binary mask
no_contrast_sok: (a boolean)
        do not fail if no contrasts specified
no_est_fwhm: (a boolean)
        turn off FWHM output estimation
no_mask_smooth: (a boolean)
        do not mask when smoothing
no_prune: (a boolean)
        do not prune
        mutually_exclusive: prunethresh
one_sample: (a boolean)
        construct X and C as a one-sample group mean
        mutually_exclusive: one_sample, fsgd, design, contrast
pca: (a boolean)
        perform pca/svd analysis on residual
per_voxel_reg: (an existing file name)
        per-voxel regressors
profile: (an integer)
        niters : test speed
prune: (a boolean)
        remove voxels that do not have a non-zero value at each frame (def)
prune_thresh: (a float)
        prune threshold. Default is FLT_MIN
        mutually_exclusive: noprune
resynth_test: (an integer)
        test GLM by resynthsis
save_cond: (a boolean)
        flag to save design matrix condition at each voxel
save_estimate: (a boolean)
        save signal estimate (yhat)
save_res_corr_mtx: (a boolean)
        save residual error spatial correlation matrix (eres.scm). Big!
save_residual: (a boolean)
        save residual error (eres)
seed: (an integer)
        used for synthesizing noise
self_reg: (a tuple of the form: (an integer, an integer, an integer))
        self-regressor from index col row slice
sim_done_file: (a file name)
        create file when simulation finished
sim_sign: ('abs' or 'pos' or 'neg')
        abs, pos, or neg
simulation: (a tuple of the form: ('perm' or 'mc-full' or 'mc-z', an integer, a float, a
         string))
        nulltype nsim thresh csdbasename
subject_id: (a string)
        subject id for surface geometry
subjects_dir: (an existing directory name)
        subjects directory
surf: (a boolean)
        analysis is on a surface mesh
        requires: subject_id, hemi
surf_geo: (a string, nipype default value: white)
        surface geometry name (e.g. white, pial)
synth: (a boolean)
        replace input with gaussian
uniform: (a tuple of the form: (a float, a float))
        use uniform distribution instead of gaussian
var_fwhm: (a float)
        smooth variance by fwhm
vox_dump: (a tuple of the form: (an integer, an integer, an integer))
        dump voxel GLM and exit
weight_file: (an existing file name)
        weight for each input at each voxel
        mutually_exclusive: weighted_ls
weight_inv: (a boolean)
        invert weights
        mutually_exclusive: weighted_ls
weight_sqrt: (a boolean)
        sqrt of weights
        mutually_exclusive: weighted_ls
weighted_ls: (an existing file name)
        weighted least squares
        mutually_exclusive: weight_file, weight_inv, weight_sqrt

Outputs:

beta_file: (an existing file name)
        map of regression coefficients
dof_file: (a file name)
        text file with effective degrees-of-freedom for the analysis
error_file: (a file name)
        map of residual error
error_stddev_file: (a file name)
        map of residual error standard deviation
error_var_file: (a file name)
        map of residual error variance
estimate_file: (a file name)
        map of the estimated Y values
frame_eigenvectors: (a file name)
        matrix of frame eigenvectors from residual PCA
ftest_file
        map of test statistic values
fwhm_file: (a file name)
        text file with estimated smoothness
gamma_file
        map of contrast of regression coefficients
gamma_var_file
        map of regression contrast variance
glm_dir: (an existing directory name)
        output directory
mask_file: (a file name)
        map of the mask used in the analysis
sig_file
        map of F-test significance (in -log10p)
singular_values: (a file name)
        matrix singular values from residual PCA
spatial_eigenvectors: (a file name)
        map of spatial eigenvectors from residual PCA
svd_stats_file: (a file name)
        text file summarizing the residual PCA

Label2Vol

Code: http://github.com/nipy/nipype/blob/master/nipype/interfaces/freesurfer/model.py#L736

Wraps command mri_label2vol

Make a binary volume from a Freesurfer label

Examples

>>> binvol = Label2Vol(label_file='cortex.label', template_file='structural.nii', reg_file='register.dat', fill_thresh=0.5, vol_label_file='foo_out.nii')
>>> binvol.cmdline
'mri_label2vol --fillthresh 0 --label cortex.label --reg register.dat --temp structural.nii --o foo_out.nii'

Inputs:

[Mandatory]
annot_file: (an existing file name)
        surface annotation file
        mutually_exclusive: label_file, annot_file, seg_file, aparc_aseg
        requires: subjectid, hemi
aparc_aseg: (a boolean)
        use aparc+aseg.mgz in subjectdir as seg
        mutually_exclusive: label_file, annot_file, seg_file, aparc_aseg
label_file: (an existing file name)
        list of label files
        mutually_exclusive: label_file, annot_file, seg_file, aparc_aseg
seg_file: (an existing file name)
        segmentation file
        mutually_exclusive: label_file, annot_file, seg_file, aparc_aseg
template_file: (an existing file name)
        output template volume

[Optional]
args: (a string)
        Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
         are a value of type 'str', nipype default value: {})
        Environment variables
fill_thresh: (0.0 <= a floating point number <= 1.0)
        thresh : between 0 and 1
hemi: ('lh' or 'rh')
        hemisphere to use lh or rh
identity: (a boolean)
        set R=I
        mutually_exclusive: reg_file, reg_header, identity
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run
invert_mtx: (a boolean)
        Invert the registration matrix
label_hit_file: (a file name)
        file with each frame is nhits for a label
label_voxel_volume: (a float)
        volume of each label point (def 1mm3)
map_label_stat: (a file name)
        map the label stats field into the vol
native_vox2ras: (a boolean)
        use native vox2ras xform instead of  tkregister-style
proj: (a tuple of the form: ('abs' or 'frac', a float, a float, a float))
        project along surface normal
reg_file: (an existing file name)
        tkregister style matrix VolXYZ = R*LabelXYZ
        mutually_exclusive: reg_file, reg_header, identity
reg_header: (an existing file name)
        label template volume
        mutually_exclusive: reg_file, reg_header, identity
subject_id: (a string)
        subject id
subjects_dir: (an existing directory name)
        subjects directory
surface: (a string)
        use surface instead of white
vol_label_file: (a file name)
        output volume

Outputs:

vol_label_file: (an existing file name)
        output volume

MRISPreproc

Code: http://github.com/nipy/nipype/blob/master/nipype/interfaces/freesurfer/model.py#L77

Wraps command mris_preproc

Use FreeSurfer mris_preproc to prepare a group of contrasts for a second level analysis

Examples

>>> preproc = MRISPreproc()
>>> preproc.inputs.target = 'fsaverage'
>>> preproc.inputs.hemi = 'lh'
>>> preproc.inputs.vol_measure_file = [('cont1.nii', 'register.dat'),                                            ('cont1a.nii', 'register.dat')]
>>> preproc.inputs.out_file = 'concatenated_file.mgz'
>>> preproc.cmdline
'mris_preproc --hemi lh --out concatenated_file.mgz --target fsaverage --iv cont1.nii register.dat --iv cont1a.nii register.dat'

Inputs:

[Mandatory]
hemi: ('lh' or 'rh')
        hemisphere for source and target
target: (a string)
        target subject name

[Optional]
args: (a string)
        Additional parameters to the command
environ: (a dictionary with keys which are a value of type 'str' and with values which
         are a value of type 'str', nipype default value: {})
        Environment variables
fsgd_file: (an existing file name)
        specify subjects using fsgd file
        mutually_exclusive: subjects, fsgd_file, subject_file
fwhm: (a float)
        smooth by fwhm mm on the target surface
        mutually_exclusive: num_iters
fwhm_source: (a float)
        smooth by fwhm mm on the source surface
        mutually_exclusive: num_iters_source
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run
num_iters: (an integer)
        niters : smooth by niters on the target surface
        mutually_exclusive: fwhm
num_iters_source: (an integer)
        niters : smooth by niters on the source surface
        mutually_exclusive: fwhm_source
out_file: (a file name)
        output filename
proj_frac: (a float)
        projection fraction for vol2surf
smooth_cortex_only: (a boolean)
        only smooth cortex (ie, exclude medial wall)
source_format: (a string)
        source format
subject_file: (an existing file name)
        file specifying subjects separated by white space
        mutually_exclusive: subjects, fsgd_file, subject_file
subjects: (a list of items which are any value)
        subjects from who measures are calculated
        mutually_exclusive: subjects, fsgd_file, subject_file
subjects_dir: (an existing directory name)
        subjects directory
surf_area: (a string)
        Extract vertex area from subject/surf/hemi.surfname to use as input.
        mutually_exclusive: surf_measure, surf_measure_file, surf_area
surf_dir: (a string)
        alternative directory (instead of surf)
surf_measure: (a string)
        Use subject/surf/hemi.surf_measure as input
        mutually_exclusive: surf_measure, surf_measure_file, surf_area
surf_measure_file: (an existing file name)
        file alternative to surfmeas, still requires list of subjects
        mutually_exclusive: surf_measure, surf_measure_file, surf_area
vol_measure_file: (a tuple of the form: (an existing file name, an existing file name))
        list of volume measure and reg file tuples

Outputs:

out_file: (an existing file name)
        preprocessed output file

OneSampleTTest

Code: http://github.com/nipy/nipype/blob/master/nipype/interfaces/freesurfer/model.py#L333

Wraps command mri_glmfit

Inputs:

[Mandatory]
in_file: (a file name)
        input 4D file

[Optional]
allow_ill_cond: (a boolean)
        allow ill-conditioned design matrices
allow_repeated_subjects: (a boolean)
        allow subject names to repeat in the fsgd file (must appear before --fsgd
args: (a string)
        Additional parameters to the command
calc_AR1: (a boolean)
        compute and save temporal AR1 of residual
check_opts: (a boolean)
        don't run anything, just check options and exit
compute_log_y: (a boolean)
        compute natural log of y prior to analysis
contrast: (an existing file name)
        contrast file
cortex: (a boolean)
        use subjects ?h.cortex.label as label
        mutually_exclusive: label_file
debug: (a boolean)
        turn on debugging
design: (an existing file name)
        design matrix file
        mutually_exclusive: fsgd, design, one_sample
diag: (an integer)
        Gdiag_no : set diagnositc level
diag_cluster: (a boolean)
        save sig volume and exit from first sim loop
environ: (a dictionary with keys which are a value of type 'str' and with values which
         are a value of type 'str', nipype default value: {})
        Environment variables
fixed_fx_dof: (an integer)
        dof for fixed effects analysis
        mutually_exclusive: fixed_fx_dof_file
fixed_fx_dof_file: (a file name)
        text file with dof for fixed effects analysis
        mutually_exclusive: fixed_fx_dof
fixed_fx_var: (an existing file name)
        for fixed effects analysis
force_perm: (a boolean)
        force perumtation test, even when design matrix is not orthog
fsgd: (a tuple of the form: (an existing file name, 'doss' or 'dods'))
        freesurfer descriptor file
        mutually_exclusive: fsgd, design, one_sample
fwhm: (a float)
        smooth input by fwhm
glm_dir: (a string)
        save outputs to dir
hemi: ('lh' or 'rh')
        surface hemisphere
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run
invert_mask: (a boolean)
        invert mask
label_file: (an existing file name)
        use label as mask, surfaces only
        mutually_exclusive: cortex
mask_file: (an existing file name)
        binary mask
no_contrast_sok: (a boolean)
        do not fail if no contrasts specified
no_est_fwhm: (a boolean)
        turn off FWHM output estimation
no_mask_smooth: (a boolean)
        do not mask when smoothing
no_prune: (a boolean)
        do not prune
        mutually_exclusive: prunethresh
one_sample: (a boolean)
        construct X and C as a one-sample group mean
        mutually_exclusive: one_sample, fsgd, design, contrast
pca: (a boolean)
        perform pca/svd analysis on residual
per_voxel_reg: (an existing file name)
        per-voxel regressors
profile: (an integer)
        niters : test speed
prune: (a boolean)
        remove voxels that do not have a non-zero value at each frame (def)
prune_thresh: (a float)
        prune threshold. Default is FLT_MIN
        mutually_exclusive: noprune
resynth_test: (an integer)
        test GLM by resynthsis
save_cond: (a boolean)
        flag to save design matrix condition at each voxel
save_estimate: (a boolean)
        save signal estimate (yhat)
save_res_corr_mtx: (a boolean)
        save residual error spatial correlation matrix (eres.scm). Big!
save_residual: (a boolean)
        save residual error (eres)
seed: (an integer)
        used for synthesizing noise
self_reg: (a tuple of the form: (an integer, an integer, an integer))
        self-regressor from index col row slice
sim_done_file: (a file name)
        create file when simulation finished
sim_sign: ('abs' or 'pos' or 'neg')
        abs, pos, or neg
simulation: (a tuple of the form: ('perm' or 'mc-full' or 'mc-z', an integer, a float, a
         string))
        nulltype nsim thresh csdbasename
subject_id: (a string)
        subject id for surface geometry
subjects_dir: (an existing directory name)
        subjects directory
surf: (a boolean)
        analysis is on a surface mesh
        requires: subject_id, hemi
surf_geo: (a string, nipype default value: white)
        surface geometry name (e.g. white, pial)
synth: (a boolean)
        replace input with gaussian
uniform: (a tuple of the form: (a float, a float))
        use uniform distribution instead of gaussian
var_fwhm: (a float)
        smooth variance by fwhm
vox_dump: (a tuple of the form: (an integer, an integer, an integer))
        dump voxel GLM and exit
weight_file: (an existing file name)
        weight for each input at each voxel
        mutually_exclusive: weighted_ls
weight_inv: (a boolean)
        invert weights
        mutually_exclusive: weighted_ls
weight_sqrt: (a boolean)
        sqrt of weights
        mutually_exclusive: weighted_ls
weighted_ls: (an existing file name)
        weighted least squares
        mutually_exclusive: weight_file, weight_inv, weight_sqrt

Outputs:

beta_file: (an existing file name)
        map of regression coefficients
dof_file: (a file name)
        text file with effective degrees-of-freedom for the analysis
error_file: (a file name)
        map of residual error
error_stddev_file: (a file name)
        map of residual error standard deviation
error_var_file: (a file name)
        map of residual error variance
estimate_file: (a file name)
        map of the estimated Y values
frame_eigenvectors: (a file name)
        matrix of frame eigenvectors from residual PCA
ftest_file
        map of test statistic values
fwhm_file: (a file name)
        text file with estimated smoothness
gamma_file
        map of contrast of regression coefficients
gamma_var_file
        map of regression contrast variance
glm_dir: (an existing directory name)
        output directory
mask_file: (a file name)
        map of the mask used in the analysis
sig_file
        map of F-test significance (in -log10p)
singular_values: (a file name)
        matrix singular values from residual PCA
spatial_eigenvectors: (a file name)
        map of spatial eigenvectors from residual PCA
svd_stats_file: (a file name)
        text file summarizing the residual PCA

SegStats

Code: http://github.com/nipy/nipype/blob/master/nipype/interfaces/freesurfer/model.py#L612

Wraps command mri_segstats

Use FreeSurfer mri_segstats for ROI analysis

Examples

>>> import nipype.interfaces.freesurfer as fs
>>> ss = fs.SegStats()
>>> ss.inputs.annot = ('PWS04', 'lh', 'aparc')
>>> ss.inputs.in_file = 'functional.nii'
>>> ss.inputs.subjects_dir = '.'
>>> ss.inputs.avgwf_txt_file = './avgwf.txt'
>>> ss.inputs.summary_file = './summary.stats'
>>> ss.cmdline
'mri_segstats --annot PWS04 lh aparc --avgwf ./avgwf.txt --i functional.nii --sum ./summary.stats'

Inputs:

[Mandatory]
annot: (a tuple of the form: (a string, 'lh' or 'rh', a string))
        subject hemi parc : use surface parcellation
        mutually_exclusive: segmentation_file, annot, surf_label
segmentation_file: (an existing file name)
        segmentation volume path
        mutually_exclusive: segmentation_file, annot, surf_label
surf_label: (a tuple of the form: (a string, 'lh' or 'rh', a string))
        subject hemi label : use surface label
        mutually_exclusive: segmentation_file, annot, surf_label

[Optional]
args: (a string)
        Additional parameters to the command
avgwf_file: (a boolean or a file name)
        Save as binary volume (bool or filename)
avgwf_txt_file: (a boolean or a file name)
        Save average waveform into file (bool or filename)
brain_vol: ('brain-vol-from-seg' or 'brainmask' or '--%s')
        Compute brain volume either with ``brainmask`` or ``brain-vol-from-seg``
calc_power: ('sqr' or 'sqrt')
        Compute either the sqr or the sqrt of the input
calc_snr: (a boolean)
        save mean/std as extra column in output table
color_table_file: (an existing file name)
        color table file with seg id names
        mutually_exclusive: color_table_file, default_color_table, gca_color_table
cortex_vol_from_surf: (a boolean)
        Compute cortex volume from surf
default_color_table: (a boolean)
        use $FREESURFER_HOME/FreeSurferColorLUT.txt
        mutually_exclusive: color_table_file, default_color_table, gca_color_table
environ: (a dictionary with keys which are a value of type 'str' and with values which
         are a value of type 'str', nipype default value: {})
        Environment variables
etiv: (a boolean)
        Compute ICV from talairach transform
etiv_only: ('etiv' or 'old-etiv' or '--%s-only')
        Compute etiv and exit.  Use ``etiv`` or ``old-etiv``
exclude_ctx_gm_wm: (a boolean)
        exclude cortical gray and white matter
exclude_id: (an integer)
        Exclude seg id from report
frame: (an integer)
        Report stats on nth frame of input volume
gca_color_table: (an existing file name)
        get color table from GCA (CMA)
        mutually_exclusive: color_table_file, default_color_table, gca_color_table
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run
in_file: (an existing file name)
        Use the segmentation to report stats on this volume
mask_erode: (an integer)
        Erode mask by some amount
mask_file: (an existing file name)
        Mask volume (same size as seg
mask_frame: (an integer)
        Mask with this (0 based) frame of the mask volume
        requires: mask_file
mask_invert: (a boolean)
        Invert binarized mask volume
mask_sign: ('abs' or 'pos' or 'neg' or '--masksign %s')
        Sign for mask threshold: pos, neg, or abs
mask_thresh: (a float)
        binarize mask with this threshold <0.5>
multiply: (a float)
        multiply input by val
non_empty_only: (a boolean)
        Only report nonempty segmentations
partial_volume_file: (an existing file name)
        Compensate for partial voluming
segment_id: (a list of items which are any value)
        Manually specify segmentation ids
sf_avg_file: (a boolean or a file name)
        Save mean across space and time
subjects_dir: (an existing directory name)
        subjects directory
summary_file: (a file name)
        Segmentation stats summary table file
vox: (a list of items which are an integer)
        Replace seg with all 0s except at C R S (three int inputs)
wm_vol_from_surf: (a boolean)
        Compute wm volume from surf

Outputs:

avgwf_file: (a file name)
        Volume with functional statistics averaged over segs
avgwf_txt_file: (a file name)
        Text file with functional statistics averaged over segs
sf_avg_file: (a file name)
        Text file with func statistics averaged over segs and framss
summary_file: (an existing file name)
        Segmentation summary statistics table