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interfaces.ants.segmentation

Atropos

Link to code

Wraps command Atropos

A finite mixture modeling (FMM) segmentation approach with possibilities for specifying prior constraints. These prior constraints include the specification of a prior label image, prior probability images (one for each class), and/or an MRF prior to enforce spatial smoothing of the labels. Similar algorithms include FAST and SPM.

Examples

>>> from nipype.interfaces.ants import Atropos
>>> at = Atropos()
>>> at.inputs.dimension = 3
>>> at.inputs.intensity_images = 'structural.nii'
>>> at.inputs.mask_image = 'mask.nii'
>>> at.inputs.initialization = 'PriorProbabilityImages'
>>> at.inputs.prior_probability_images = ['rc1s1.nii', 'rc1s2.nii']
>>> at.inputs.number_of_tissue_classes = 2
>>> at.inputs.prior_weighting = 0.8
>>> at.inputs.prior_probability_threshold = 0.0000001
>>> at.inputs.likelihood_model = 'Gaussian'
>>> at.inputs.mrf_smoothing_factor = 0.2
>>> at.inputs.mrf_radius = [1, 1, 1]
>>> at.inputs.icm_use_synchronous_update = True
>>> at.inputs.maximum_number_of_icm_terations = 1
>>> at.inputs.n_iterations = 5
>>> at.inputs.convergence_threshold = 0.000001
>>> at.inputs.posterior_formulation = 'Socrates'
>>> at.inputs.use_mixture_model_proportions = True
>>> at.inputs.save_posteriors = True
>>> at.cmdline
'Atropos --image-dimensionality 3 --icm [1,1] --initialization PriorProbabilityImages[2,priors/priorProbImages%02d.nii,0.8,1e-07] --intensity-image structural.nii --likelihood-model Gaussian --mask-image mask.nii --mrf [0.2,1x1x1] --convergence [5,1e-06] --output [structural_labeled.nii,POSTERIOR_%02d.nii.gz] --posterior-formulation Socrates[1]'

Inputs:

[Mandatory]
initialization: ('Random' or 'Otsu' or 'KMeans' or 'PriorProbabilityImages' or
         'PriorLabelImage')
        requires: number_of_tissue_classes
mask_image: (an existing file name)
number_of_tissue_classes: (an integer)
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output

[Optional]
args: (a string)
        Additional parameters to the command
convergence_threshold: (a float)
        requires: n_iterations
dimension: (3 or 2 or 4, nipype default value: 3)
        image dimension (2, 3, or 4)
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
icm_use_synchronous_update: (a boolean)
ignore_exception: (a boolean, nipype default value: False)
        Print an error message instead of throwing an exception in case the interface fails to
        run
intensity_images: (an existing file name)
likelihood_model: (a string)
maximum_number_of_icm_terations: (an integer)
        requires: icm_use_synchronous_update
mrf_radius: (a list of items which are an integer)
        requires: mrf_smoothing_factor
mrf_smoothing_factor: (a float)
n_iterations: (an integer)
num_threads: (an integer, nipype default value: -1)
        Number of ITK threads to use
out_classified_image_name: (a file name)
output_posteriors_name_template: (a string, nipype default value: POSTERIOR_%02d.nii.gz)
posterior_formulation: (a string)
prior_probability_images: (an existing file name)
prior_probability_threshold: (a float)
        requires: prior_weighting
prior_weighting: (a float)
save_posteriors: (a boolean)
use_mixture_model_proportions: (a boolean)
        requires: posterior_formulation

Outputs:

classified_image: (an existing file name)
posteriors: (a file name)

N4BiasFieldCorrection

Link to code

Wraps command N4BiasFieldCorrection

N4 is a variant of the popular N3 (nonparameteric nonuniform normalization) retrospective bias correction algorithm. Based on the assumption that the corruption of the low frequency bias field can be modeled as a convolution of the intensity histogram by a Gaussian, the basic algorithmic protocol is to iterate between deconvolving the intensity histogram by a Gaussian, remapping the intensities, and then spatially smoothing this result by a B-spline modeling of the bias field itself. The modifications from and improvements obtained over the original N3 algorithm are described in the following paper: N. Tustison et al., N4ITK: Improved N3 Bias Correction, IEEE Transactions on Medical Imaging, 29(6):1310-1320, June 2010.

Examples

>>> from nipype.interfaces.ants import N4BiasFieldCorrection
>>> n4 = N4BiasFieldCorrection()
>>> n4.inputs.dimension = 3
>>> n4.inputs.input_image = 'structural.nii'
>>> n4.inputs.bspline_fitting_distance = 300
>>> n4.inputs.shrink_factor = 3
>>> n4.inputs.n_iterations = [50,50,30,20]
>>> n4.inputs.convergence_threshold = 1e-6
>>> n4.cmdline
'N4BiasFieldCorrection --convergence [ 50x50x30x20 ,1e-06] --bsline-fitting [300] --image-dimension 3 --input-image structural.nii --output structural_corrected.nii --shrink-factor 3'

Inputs:

[Mandatory]
input_image: (a file name)
        image to apply transformation to (generally a coregistered functional)
terminal_output: ('stream' or 'allatonce' or 'file' or 'none')
        Control terminal output

[Optional]
args: (a string)
        Additional parameters to the command
bspline_fitting_distance: (a float)
convergence_threshold: (a float)
        requires: n_iterations
dimension: (3 or 2, nipype default value: 3)
        image dimension (2 or 3)
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
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: (a file name)
n_iterations: (a list of items which are an integer)
        requires: convergence_threshold
num_threads: (an integer, nipype default value: -1)
        Number of ITK threads to use
output_image: (a string)
        output file name
shrink_factor: (an integer)

Outputs:

output_image: (an existing file name)
        Warped image