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# interfaces.spm.preprocess

## ApplyDeformations

Link to code

Inputs:

    [Mandatory]
    deformation_field: (an existing file name)
    in_files: (an existing file name)
    reference_volume: (an existing file name)

    [Optional]
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    interp: (0 <= an integer <= 7)
            degree of b-spline used for interpolation
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    paths: (a directory name)
            Paths to add to matlabpath
    use_mcr: (a boolean)
            Run m-code using SPM MCR

Outputs:

    out_files: (an existing file name)

## Coregister

Link to code

Use spm_coreg for estimating cross-modality rigid body alignment

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=39

### Examples

    >>> import nipype.interfaces.spm as spm
    >>> coreg = spm.Coregister()
    >>> coreg.inputs.target = 'functional.nii'
    >>> coreg.inputs.source = 'structural.nii'
    >>> coreg.run()

Inputs:

    [Mandatory]
    source: (an existing file name)
            file to register to target
    target: (an existing file name)
            reference file to register to

    [Optional]
    apply_to_files: (an existing file name)
            files to apply transformation to
    cost_function: ('mi' or 'nmi' or 'ecc' or 'ncc')
            cost function, one of: 'mi' - Mutual Information,
                            'nmi' - Normalised Mutual Information,
                            'ecc' - Entropy Correlation Coefficient,
                            'ncc' - Normalised Cross Correlation
    fwhm: (a float)
            gaussian smoothing kernel width (mm)
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    jobtype: ('estwrite' or 'estimate' or 'write', nipype default value: estwrite)
            one of: estimate, write, estwrite
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    out_prefix: (a string, nipype default value: r)
            coregistered output prefix
    paths: (a directory name)
            Paths to add to matlabpath
    separation: (a list of items which are a float)
            sampling separation in mm
    tolerance: (a list of items which are a float)
            acceptable tolerance for each of 12 params
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    write_interp: (an integer >= 0)
            degree of b-spline used for interpolation
    write_mask: (a boolean)
            True/False mask output image
    write_wrap: (a list of from 3 to 3 items which are an integer)
            Check if interpolation should wrap in [x,y,z]

Outputs:

    coregistered_files: (an existing file name)
            Coregistered other files
    coregistered_source: (an existing file name)
            Coregistered source files

## CreateWarped

Link to code

Apply a flow field estimated by DARTEL to create warped images

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=202

### Examples

    >>> import nipype.interfaces.spm as spm
    >>> create_warped = spm.CreateWarped()
    >>> create_warped.inputs.image_files = ['rc1s1.nii', 'rc1s2.nii']
    >>> create_warped.inputs.flowfield_files = ['u_rc1s1_Template.nii', 'u_rc1s2_Template.nii']
    >>> create_warped.run()

Inputs:

    [Mandatory]
    flowfield_files: (an existing file name)
            DARTEL flow fields u_rc1*
    image_files: (an existing file name)
            A list of files to be warped

    [Optional]
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    interp: (0 <= an integer <= 7)
            degree of b-spline used for interpolation
    iterations: (0 <= an integer <= 9)
            The number of iterations: log2(number of time steps)
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    paths: (a directory name)
            Paths to add to matlabpath
    use_mcr: (a boolean)
            Run m-code using SPM MCR

Outputs:

    warped_files: (a list of items which are an existing file name)

## DARTEL

Link to code

Use spm DARTEL to create a template and flow fields

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=197

### Examples

    >>> import nipype.interfaces.spm as spm
    >>> dartel = spm.DARTEL()
    >>> dartel.inputs.image_files = [['rc1s1.nii','rc1s2.nii'],['rc2s1.nii', 'rc2s2.nii']]
    >>> dartel.run()

Inputs:

    [Mandatory]
    image_files: (a list of items which are a list of items which are an existing file name)
            A list of files to be segmented

    [Optional]
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    iteration_parameters: (a list of from 3 to 12 items which are a tuple of the form: (1 <=
             an integer <= 10, a tuple of the form: (a float, a float, a float), 1 or 2 or 4 or 8 or
             16 or 32 or 64 or 128 or 256 or 512, 0 or 0.5 or 1 or 2 or 4 or 8 or 16 or 32))
            List of tuples for each iteration
                                                   - Inner iterations
                                                   - Regularization parameters
                                                   - Time points for deformation model
                                                   - smoothing parameter
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    optimization_parameters: (a tuple of the form: (a float, 1 <= an integer <= 8, 1 <= an
             integer <= 8))
            Optimization settings a tuple
                                                       - LM regularization
                                                       - cycles of multigrid solver
                                                       - relaxation iterations
    paths: (a directory name)
            Paths to add to matlabpath
    regularization_form: ('Linear' or 'Membrane' or 'Bending')
            Form of regularization energy term
    template_prefix: (a string, nipype default value: Template)
            Prefix for template
    use_mcr: (a boolean)
            Run m-code using SPM MCR

Outputs:

    dartel_flow_fields: (a list of items which are an existing file name)
            DARTEL flow fields
    final_template_file: (an existing file name)
            final DARTEL template
    template_files: (a list of items which are an existing file name)
            Templates from different stages of iteration

## DARTELNorm2MNI

Link to code

Use spm DARTEL to normalize data to MNI space

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=200

### Examples

    >>> import nipype.interfaces.spm as spm
    >>> nm = spm.DARTELNorm2MNI()
    >>> nm.inputs.template_file = 'Template_6.nii'
    >>> nm.inputs.flowfield_files = ['u_rc1s1_Template.nii', 'u_rc1s3_Template.nii']
    >>> nm.inputs.apply_to_files = ['c1s1.nii', 'c1s3.nii']
    >>> nm.inputs.modulate = True
    >>> nm.run()

Inputs:

    [Mandatory]
    apply_to_files: (an existing file name)
            Files to apply the transform to
    flowfield_files: (an existing file name)
            DARTEL flow fields u_rc1*
    template_file: (an existing file name)
            DARTEL template

    [Optional]
    bounding_box: (a tuple of the form: (a float, a float, a float, a float, a float, a
             float))
            Voxel sizes for output file
    fwhm: (a list of from 3 to 3 items which are a float or a float)
            3-list of fwhm for each dimension
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    modulate: (a boolean)
            Modulate out images - no modulation preserves concentrations
    paths: (a directory name)
            Paths to add to matlabpath
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    voxel_size: (a tuple of the form: (a float, a float, a float))
            Voxel sizes for output file

Outputs:

    normalization_parameter_file: (an existing file name)
            Transform parameters to MNI space
    normalized_files: (an existing file name)
            Normalized files in MNI space

## NewSegment

Link to code

Use spm_preproc8 (New Segment) to separate structural images into different tissue classes. Supports multiple modalities.

NOTE: This interface currently supports single channel input only

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=185

### Examples

    >>> import nipype.interfaces.spm as spm
    >>> seg = spm.NewSegment()
    >>> seg.inputs.channel_files = 'structural.nii'
    >>> seg.inputs.channel_info = (0.0001, 60, (True, True))
    >>> seg.run()

For VBM pre-processing [http://www.fil.ion.ucl.ac.uk/~john/misc/VBMclass10.pdf], TPM.nii should be replaced by /path/to/spm8/toolbox/Seg/TPM.nii

    >>> seg = NewSegment()
    >>> seg.inputs.channel_files = 'structural.nii'
    >>> tissue1 = (('TPM.nii', 1), 2, (True,True), (False, False))
    >>> tissue2 = (('TPM.nii', 2), 2, (True,True), (False, False))
    >>> tissue3 = (('TPM.nii', 3), 2, (True,False), (False, False))
    >>> tissue4 = (('TPM.nii', 4), 2, (False,False), (False, False))
    >>> tissue5 = (('TPM.nii', 5), 2, (False,False), (False, False))
    >>> seg.inputs.tissues = [tissue1, tissue2, tissue3, tissue4, tissue5]
    >>> seg.run()

Inputs:

    [Mandatory]
    channel_files: (an existing file name)
            A list of files to be segmented

    [Optional]
    affine_regularization: ('mni' or 'eastern' or 'subj' or 'none')
            mni, eastern, subj, none
    channel_info: (a tuple of the form: (a float, a float, a tuple of the form: (a boolean, a
             boolean)))
            A tuple with the following fields:
                        - bias reguralisation (0-10)
                        - FWHM of Gaussian smoothness of bias
                        - which maps to save (Corrected, Field) - a tuple of two boolean values
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    paths: (a directory name)
            Paths to add to matlabpath
    sampling_distance: (a float)
            Sampling distance on data for parameter estimation
    tissues: (a list of items which are a tuple of the form: (a tuple of the form: (an
             existing file name, an integer), an integer, a tuple of the form: (a boolean, a
             boolean), a tuple of the form: (a boolean, a boolean)))
            A list of tuples (one per tissue) with the following fields:
                        - tissue probability map (4D), 1-based index to frame
                        - number of gaussians
                        - which maps to save [Native, DARTEL] - a tuple of two boolean values
                        - which maps to save [Modulated, Unmodualted] - a tuple of two boolean
            values
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    warping_regularization: (a float)
            Aproximate distance between sampling points.
    write_deformation_fields: (a list of from 2 to 2 items which are a boolean)
            Which deformation fields to write:[Inverse, Forward]

Outputs:

    bias_corrected_images: (an existing file name)
            bias corrected images
    bias_field_images: (an existing file name)
            bias field images
    dartel_input_images: (a list of items which are a list of items which are an existing
             file name)
            dartel imported class images
    forward_deformation_field: (an existing file name)
    inverse_deformation_field: (an existing file name)
    modulated_class_images: (a list of items which are a list of items which are an existing
             file name)
            modulated+normalized class images
    native_class_images: (a list of items which are a list of items which are an existing
             file name)
            native space probability maps
    normalized_class_images: (a list of items which are a list of items which are an existing
             file name)
            normalized class images
    transformation_mat: (an existing file name)
            Normalization transformation

## Normalize

Link to code

use spm_normalise for warping an image to a template

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=51

### Examples

    >>> import nipype.interfaces.spm as spm
    >>> norm = spm.Normalize()
    >>> norm.inputs.source = 'functional.nii'
    >>> norm.run()

Inputs:

    [Mandatory]
    parameter_file: (a file name)
            normalization parameter file*_sn.mat
            mutually_exclusive: source, template
    source: (an existing file name)
            file to normalize to template
            mutually_exclusive: parameter_file
    template: (an existing file name)
            template file to normalize to
            mutually_exclusive: parameter_file

    [Optional]
    DCT_period_cutoff: (a float)
            Cutoff of for DCT bases (opt)
    affine_regularization_type: ('mni' or 'size' or 'none')
            mni, size, none (opt)
    apply_to_files: (an existing file name)
            files to apply transformation to (opt)
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    jobtype: ('estwrite' or 'est' or 'write', nipype default value: estwrite)
            one of: est, write, estwrite (opt, estwrite)
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    nonlinear_iterations: (an integer)
            Number of iterations of nonlinear warping (opt)
    nonlinear_regularization: (a float)
            the amount of the regularization for the nonlinear part of the normalization (opt)
    out_prefix: (a string, nipype default value: w)
            normalized output prefix
    paths: (a directory name)
            Paths to add to matlabpath
    source_image_smoothing: (a float)
            source smoothing (opt)
    source_weight: (a file name)
            name of weighting image for source (opt)
    template_image_smoothing: (a float)
            template smoothing (opt)
    template_weight: (a file name)
            name of weighting image for template (opt)
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    write_bounding_box: (a list of from 2 to 2 items which are a list of from 3 to 3 items
             which are a float)
            3x2-element list of lists (opt)
    write_interp: (an integer >= 0)
            degree of b-spline used for interpolation
    write_preserve: (a boolean)
            True/False warped images are modulated (opt,)
    write_voxel_sizes: (a list of from 3 to 3 items which are a float)
            3-element list (opt)
    write_wrap: (a list of items which are an integer)
            Check if interpolation should wrap in [x,y,z] - list of bools (opt)

Outputs:

    normalization_parameters: (an existing file name)
            MAT files containing the normalization parameters
    normalized_files: (an existing file name)
            Normalized other files
    normalized_source: (an existing file name)
            Normalized source files

## Realign

Link to code

Use spm_realign for estimating within modality rigid body alignment

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=25

### Examples

    >>> import nipype.interfaces.spm as spm
    >>> realign = spm.Realign()
    >>> realign.inputs.in_files = 'functional.nii'
    >>> realign.inputs.register_to_mean = True
    >>> realign.run()

Inputs:

    [Mandatory]
    in_files: (a list of items which are an existing file name or an existing file name)
            list of filenames to realign

    [Optional]
    fwhm: (a floating point number >= 0.0)
            gaussian smoothing kernel width
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    interp: (0 <= an integer <= 7)
            degree of b-spline used for interpolation
    jobtype: ('estwrite' or 'estimate' or 'write', nipype default value: estwrite)
            one of: estimate, write, estwrite
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    out_prefix: (a string, nipype default value: r)
            realigned output prefix
    paths: (a directory name)
            Paths to add to matlabpath
    quality: (0.0 <= a floating point number <= 1.0)
            0.1 = fast, 1.0 = precise
    register_to_mean: (a boolean)
            Indicate whether realignment is done to the mean image
    separation: (a floating point number >= 0.0)
            sampling separation in mm
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    weight_img: (an existing file name)
            filename of weighting image
    wrap: (a list of from 3 to 3 items which are an integer)
            Check if interpolation should wrap in [x,y,z]
    write_interp: (0 <= an integer <= 7)
            degree of b-spline used for interpolation
    write_mask: (a boolean)
            True/False mask output image
    write_which: (a list of items which are a value of type 'int', nipype default value: [1,
             1])
            determines which images to reslice
    write_wrap: (a list of from 3 to 3 items which are an integer)
            Check if interpolation should wrap in [x,y,z]

Outputs:

    mean_image: (an existing file name)
            Mean image file from the realignment
    realigned_files: (a list of items which are an existing file name or an existing file
             name)
            Realigned files
    realignment_parameters: (an existing file name)
            Estimated translation and rotation parameters

## Segment

Link to code

use spm_segment to separate structural images into different tissue classes.

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=43

### Examples

    >>> import nipype.interfaces.spm as spm
    >>> seg = spm.Segment()
    >>> seg.inputs.data = 'structural.nii'
    >>> seg.run()

Inputs:

    [Mandatory]
    data: (an existing file name)
            one scan per subject

    [Optional]
    affine_regularization: ('mni' or 'eastern' or 'subj' or 'none' or '')
            Possible options: "mni", "eastern", "subj", "none" (no reguralisation), "" (no affine
            registration)
    bias_fwhm: (30 or 40 or 50 or 60 or 70 or 80 or 90 or 100 or 110 or 120 or 130 or 'Inf')
            FWHM of Gaussian smoothness of bias
    bias_regularization: (0 or 1e-05 or 0.0001 or 0.001 or 0.01 or 0.1 or 1 or 10)
            no(0) - extremely heavy (10)
    clean_masks: ('no' or 'light' or 'thorough')
            clean using estimated brain mask ('no','light','thorough')
    csf_output_type: (a list of from 3 to 3 items which are a boolean)
            Options to produce CSF images: c3*.img, wc3*.img and mwc3*.img.
                        None: [False,False,False],
                        Native Space: [False,False,True],
                        Unmodulated Normalised: [False,True,False],
                        Modulated Normalised: [True,False,False],
                        Native + Unmodulated Normalised: [False,True,True],
                        Native + Modulated Normalised: [True,False,True],
                        Native + Modulated + Unmodulated: [True,True,True],
                        Modulated + Unmodulated Normalised: [True,True,False]
    gaussians_per_class: (a list of items which are an integer)
            num Gaussians capture intensity distribution
    gm_output_type: (a list of from 3 to 3 items which are a boolean)
            Options to produce grey matter images: c1*.img, wc1*.img and mwc1*.img.
                        None: [False,False,False],
                        Native Space: [False,False,True],
                        Unmodulated Normalised: [False,True,False],
                        Modulated Normalised: [True,False,False],
                        Native + Unmodulated Normalised: [False,True,True],
                        Native + Modulated Normalised: [True,False,True],
                        Native + Modulated + Unmodulated: [True,True,True],
                        Modulated + Unmodulated Normalised: [True,True,False]
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    mask_image: (an existing file name)
            Binary image to restrict parameter estimation
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    paths: (a directory name)
            Paths to add to matlabpath
    sampling_distance: (a float)
            Sampling distance on data for parameter estimation
    save_bias_corrected: (a boolean)
            True/False produce a bias corrected image
    tissue_prob_maps: (a list of items which are an existing file name)
            list of gray, white & csf prob. (opt,)
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    warp_frequency_cutoff: (a float)
            Cutoff of DCT bases
    warping_regularization: (a float)
            Controls balance between parameters and data
    wm_output_type: (a list of from 3 to 3 items which are a boolean)
            Options to produce white matter images: c2*.img, wc2*.img and mwc2*.img.
                        None: [False,False,False],
                        Native Space: [False,False,True],
                        Unmodulated Normalised: [False,True,False],
                        Modulated Normalised: [True,False,False],
                        Native + Unmodulated Normalised: [False,True,True],
                        Native + Modulated Normalised: [True,False,True],
                        Native + Modulated + Unmodulated: [True,True,True],
                        Modulated + Unmodulated Normalised: [True,True,False]

Outputs:

    inverse_transformation_mat: (an existing file name)
            Inverse normalization info
    modulated_csf_image: (an existing file name)
            modulated, normalized csf probability map
    modulated_gm_image: (an existing file name)
            modulated, normalized grey probability map
    modulated_input_image: (an existing file name)
            modulated version of input image
    modulated_wm_image: (an existing file name)
            modulated, normalized white probability map
    native_csf_image: (an existing file name)
            native space csf probability map
    native_gm_image: (an existing file name)
            native space grey probability map
    native_wm_image: (an existing file name)
            native space white probability map
    normalized_csf_image: (an existing file name)
            normalized csf probability map
    normalized_gm_image: (an existing file name)
            normalized grey probability map
    normalized_wm_image: (an existing file name)
            normalized white probability map
    transformation_mat: (an existing file name)
            Normalization transformation

## SliceTiming

Link to code

Use spm to perform slice timing correction.

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=19

### Examples

    >>> from nipype.interfaces.spm import SliceTiming
    >>> st = SliceTiming()
    >>> st.inputs.in_files = 'functional.nii'
    >>> st.inputs.num_slices = 32
    >>> st.inputs.time_repetition = 6.0
    >>> st.inputs.time_acquisition = 6. - 6./32.
    >>> st.inputs.slice_order = range(32,0,-1)
    >>> st.inputs.ref_slice = 1
    >>> st.run()

Inputs:

    [Mandatory]
    in_files: (a list of items which are an existing file name or an existing file name)
            list of filenames to apply slice timing
    num_slices: (an integer)
            number of slices in a volume
    ref_slice: (an integer)
            1-based Number of the reference slice
    slice_order: (a list of items which are an integer)
            1-based order in which slices are acquired
    time_acquisition: (a float)
            time of volume acquisition. usually calculated as TR-(TR/num_slices)
    time_repetition: (a float)
            time between volume acquisitions (start to start time)

    [Optional]
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    out_prefix: (a string, nipype default value: a)
            slicetimed output prefix
    paths: (a directory name)
            Paths to add to matlabpath
    use_mcr: (a boolean)
            Run m-code using SPM MCR

Outputs:

    timecorrected_files: (a file name)

## Smooth

Link to code

Use spm_smooth for 3D Gaussian smoothing of image volumes.

http://www.fil.ion.ucl.ac.uk/spm/doc/manual.pdf#page=57

### Examples

    >>> import nipype.interfaces.spm as spm
    >>> smooth = spm.Smooth()
    >>> smooth.inputs.in_files = 'functional.nii'
    >>> smooth.inputs.fwhm = [4, 4, 4]
    >>> smooth.run()

Inputs:

    [Mandatory]
    in_files: (an existing file name)
            list of files to smooth

    [Optional]
    data_type: (an integer)
            Data type of the output images (opt)
    fwhm: (a list of from 3 to 3 items which are a float or a float)
            3-list of fwhm for each dimension (opt)
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    implicit_masking: (a boolean)
            A mask implied by a particular voxel value
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    out_prefix: (a string, nipype default value: s)
            smoothed output prefix
    paths: (a directory name)
            Paths to add to matlabpath
    use_mcr: (a boolean)
            Run m-code using SPM MCR

Outputs:

    smoothed_files: (an existing file name)
            smoothed files

## VBMSegment

Link to code

Use VBM8 toolbox to separate structural images into different tissue classes.

### Example

    >>> import nipype.interfaces.spm as spm
    >>> seg = spm.VBMSegment()
    >>> seg.inputs.tissues = 'TPM.nii'
    >>> seg.inputs.dartel_template = 'Template_1_IXI550_MNI152.nii'
    >>> seg.inputs.bias_corrected_native = True
    >>> seg.inputs.gm_native = True
    >>> seg.inputs.wm_native = True
    >>> seg.inputs.csf_native = True
    >>> seg.inputs.pve_label_native = True
    >>> seg.inputs.deformation_field = (True, False)
    >>> seg.run()

Inputs:

    [Mandatory]
    in_files: (an existing file name)
            A list of files to be segmented

    [Optional]
    bias_corrected_affine: (a boolean, nipype default value: False)
    bias_corrected_native: (a boolean, nipype default value: False)
    bias_corrected_normalized: (a boolean, nipype default value: True)
    bias_fwhm: (30 or 40 or 50 or 60 or 70 or 80 or 90 or 100 or 110 or 120 or 130 or 'Inf',
             nipype default value: 60)
            FWHM of Gaussian smoothness of bias
    bias_regularization: (0 or 1e-05 or 0.0001 or 0.001 or 0.01 or 0.1 or 1 or 10, nipype
             default value: 0.0001)
            no(0) - extremely heavy (10)
    cleanup_partitions: (an integer, nipype default value: 1)
            0=None,1=light,2=thorough
    csf_dartel: (0 <= an integer <= 2, nipype default value: 0)
            0=None,1=rigid(SPM8 default),2=affine
    csf_modulated_normalized: (0 <= an integer <= 2, nipype default value: 2)
            0=none,1=affine+non-linear(SPM8 default),2=non-linear only
    csf_native: (a boolean, nipype default value: False)
    csf_normalized: (a boolean, nipype default value: False)
    dartel_template: (an existing file name)
    deformation_field: (a tuple of the form: (a boolean, a boolean), nipype default value:
             (0, 0))
            forward and inverse field
    display_results: (a boolean, nipype default value: True)
    gaussians_per_class: (a tuple of the form: (an integer, an integer, an integer, an
             integer, an integer, an integer), nipype default value: (2, 2, 2, 3, 4, 2))
            number of gaussians for each tissue class
    gm_dartel: (0 <= an integer <= 2, nipype default value: 0)
            0=None,1=rigid(SPM8 default),2=affine
    gm_modulated_normalized: (0 <= an integer <= 2, nipype default value: 2)
            0=none,1=affine+non-linear(SPM8 default),2=non-linear only
    gm_native: (a boolean, nipype default value: False)
    gm_normalized: (a boolean, nipype default value: False)
    ignore_exception: (a boolean, nipype default value: False)
            Print an error message instead of throwing an exception in case the interface fails to
            run
    jacobian_determinant: (a boolean, nipype default value: False)
    matlab_cmd: (a string)
            matlab command to use
    mfile: (a boolean, nipype default value: True)
            Run m-code using m-file
    mrf_weighting: (a float, nipype default value: 0.15)
    paths: (a directory name)
            Paths to add to matlabpath
    pve_label_dartel: (0 <= an integer <= 2, nipype default value: 0)
            0=None,1=rigid(SPM8 default),2=affine
    pve_label_native: (a boolean, nipype default value: False)
    pve_label_normalized: (a boolean, nipype default value: False)
    sampling_distance: (a float, nipype default value: 3)
            Sampling distance on data for parameter estimation
    spatial_normalization: ('high' or 'low', nipype default value: high)
    tissues: (an existing file name)
            tissue probability map
    use_mcr: (a boolean)
            Run m-code using SPM MCR
    use_sanlm_denoising_filter: (0 <= an integer <= 2, nipype default value: 2)
            0=No denoising, 1=denoising,2=denoising multi-threaded
    warping_regularization: (a float, nipype default value: 4)
            Controls balance between parameters and data
    wm_dartel: (0 <= an integer <= 2, nipype default value: 0)
            0=None,1=rigid(SPM8 default),2=affine
    wm_modulated_normalized: (0 <= an integer <= 2, nipype default value: 2)
            0=none,1=affine+non-linear(SPM8 default),2=non-linear only
    wm_native: (a boolean, nipype default value: False)
    wm_normalized: (a boolean, nipype default value: False)

Outputs:

    bias_corrected_images: (an existing file name)
            bias corrected images
    dartel_input_images: (a list of items which are a list of items which are an existing
             file name)
            dartel imported class images
    forward_deformation_field: (an existing file name)
    inverse_deformation_field: (an existing file name)
    jacobian_determinant_images: (an existing file name)
    modulated_class_images: (a list of items which are a list of items which are an existing
             file name)
            modulated+normalized class images
    native_class_images: (a list of items which are a list of items which are an existing
             file name)
            native space probability maps
    normalized_bias_corrected_images: (an existing file name)
            bias corrected images
    normalized_class_images: (a list of items which are a list of items which are an existing
             file name)
            normalized class images
    pve_label_native_images: (an existing file name)
    pve_label_normalized_images: (an existing file name)
    pve_label_registered_images: (an existing file name)
    transformation_mat: (an existing file name)
            Normalization transformation