interfaces.niftyseg.lesions¶
FillLesions¶
Wraps command seg_FillLesions
Interface for executable seg_FillLesions from NiftySeg platform.
Fill all the masked lesions with WM intensity average.
Examples¶
>>> from nipype.interfaces import niftyseg
>>> node = niftyseg.FillLesions()
>>> node.inputs.in_file = 'im1.nii'
>>> node.inputs.lesion_mask = 'im2.nii'
>>> node.cmdline
'seg_FillLesions -i im1.nii -l im2.nii -o im1_lesions_filled.nii.gz'
Inputs:
[Mandatory]
in_file: (an existing file name)
Input image to fill lesions
flag: -i %s, position: 1
lesion_mask: (an existing file name)
Lesion mask
flag: -l %s, position: 2
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
bin_mask: (a file name)
Give a binary mask with the valid search areas.
flag: -mask %s
cwf: (a float)
Patch cardinality weighting factor (by default 2).
flag: -cwf %f
debug: (a boolean)
Save all intermidium files (by default OFF).
flag: -debug
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
in_dilation: (an integer (int or long))
Dilate the mask <int> times (in voxels, by default 0)
flag: -dil %d
match: (a float)
Percentage of minimum number of voxels between patches <float> (by
default 0.5).
flag: -match %f
other: (a boolean)
Guizard et al. (FIN 2015) method, it doesn't include the
multiresolution/hierarchical inpainting part, this part needs to be
done with some external software such as reg_tools and reg_resample
from NiftyReg. By default it uses the method presented in Prados et
al. (Neuroimage 2016).
flag: -other
out_datatype: (a string)
Set output <datatype> (char, short, int, uchar, ushort, uint, float,
double).
flag: -odt %s
out_file: (a file name)
The output filename of the fill lesions results
flag: -o %s, position: 3
search: (a float)
Minimum percentage of valid voxels in target patch <float> (by
default 0).
flag: -search %f
size: (an integer (int or long))
Search regions size respect biggest patch size (by default 4).
flag: -size %d
smooth: (a float)
Smoothing by <float> (in minimal 6-neighbourhood voxels (by default
0.1)).
flag: -smo %f
use_2d: (a boolean)
Uses 2D patches in the Z axis, by default 3D.
flag: -2D
verbose: (a boolean)
Verbose (by default OFF).
flag: -v
Outputs:
out_file: (a file name)
Output segmentation