interfaces.ants.segmentation¶
AntsJointFusion¶
Wraps command antsJointFusion
Examples¶
>>> from nipype.interfaces.ants import AntsJointFusion
>>> antsjointfusion = AntsJointFusion()
>>> antsjointfusion.inputs.out_label_fusion = 'ants_fusion_label_output.nii'
>>> antsjointfusion.inputs.atlas_image = [ ['rc1s1.nii','rc1s2.nii'] ]
>>> antsjointfusion.inputs.atlas_segmentation_image = ['segmentation0.nii.gz']
>>> antsjointfusion.inputs.target_image = ['im1.nii']
>>> antsjointfusion.cmdline
"antsJointFusion -a 0.1 -g ['rc1s1.nii', 'rc1s2.nii'] -l segmentation0.nii.gz -b 2.0 -o ants_fusion_label_output.nii -s 3x3x3 -t ['im1.nii']"
>>> antsjointfusion.inputs.target_image = [ ['im1.nii', 'im2.nii'] ]
>>> antsjointfusion.cmdline
"antsJointFusion -a 0.1 -g ['rc1s1.nii', 'rc1s2.nii'] -l segmentation0.nii.gz -b 2.0 -o ants_fusion_label_output.nii -s 3x3x3 -t ['im1.nii', 'im2.nii']"
>>> antsjointfusion.inputs.atlas_image = [ ['rc1s1.nii','rc1s2.nii'],
... ['rc2s1.nii','rc2s2.nii'] ]
>>> antsjointfusion.inputs.atlas_segmentation_image = ['segmentation0.nii.gz',
... 'segmentation1.nii.gz']
>>> antsjointfusion.cmdline
"antsJointFusion -a 0.1 -g ['rc1s1.nii', 'rc1s2.nii'] -g ['rc2s1.nii', 'rc2s2.nii'] -l segmentation0.nii.gz -l segmentation1.nii.gz -b 2.0 -o ants_fusion_label_output.nii -s 3x3x3 -t ['im1.nii', 'im2.nii']"
>>> antsjointfusion.inputs.dimension = 3
>>> antsjointfusion.inputs.alpha = 0.5
>>> antsjointfusion.inputs.beta = 1.0
>>> antsjointfusion.inputs.patch_radius = [3,2,1]
>>> antsjointfusion.inputs.search_radius = [3]
>>> antsjointfusion.cmdline
"antsJointFusion -a 0.5 -g ['rc1s1.nii', 'rc1s2.nii'] -g ['rc2s1.nii', 'rc2s2.nii'] -l segmentation0.nii.gz -l segmentation1.nii.gz -b 1.0 -d 3 -o ants_fusion_label_output.nii -p 3x2x1 -s 3 -t ['im1.nii', 'im2.nii']"
>>> antsjointfusion.inputs.search_radius = ['mask.nii']
>>> antsjointfusion.inputs.verbose = True
>>> antsjointfusion.inputs.exclusion_image = ['roi01.nii', 'roi02.nii']
>>> antsjointfusion.inputs.exclusion_image_label = ['1','2']
>>> antsjointfusion.cmdline
"antsJointFusion -a 0.5 -g ['rc1s1.nii', 'rc1s2.nii'] -g ['rc2s1.nii', 'rc2s2.nii'] -l segmentation0.nii.gz -l segmentation1.nii.gz -b 1.0 -d 3 -e 1[roi01.nii] -e 2[roi02.nii] -o ants_fusion_label_output.nii -p 3x2x1 -s mask.nii -t ['im1.nii', 'im2.nii'] -v"
>>> antsjointfusion.inputs.out_label_fusion = 'ants_fusion_label_output.nii'
>>> antsjointfusion.inputs.out_intensity_fusion_name_format = 'ants_joint_fusion_intensity_%d.nii.gz'
>>> antsjointfusion.inputs.out_label_post_prob_name_format = 'ants_joint_fusion_posterior_%d.nii.gz'
>>> antsjointfusion.inputs.out_atlas_voting_weight_name_format = 'ants_joint_fusion_voting_weight_%d.nii.gz'
>>> antsjointfusion.cmdline
"antsJointFusion -a 0.5 -g ['rc1s1.nii', 'rc1s2.nii'] -g ['rc2s1.nii', 'rc2s2.nii'] -l segmentation0.nii.gz -l segmentation1.nii.gz -b 1.0 -d 3 -e 1[roi01.nii] -e 2[roi02.nii] -o [ants_fusion_label_output.nii, ants_joint_fusion_intensity_%d.nii.gz, ants_joint_fusion_posterior_%d.nii.gz, ants_joint_fusion_voting_weight_%d.nii.gz] -p 3x2x1 -s mask.nii -t ['im1.nii', 'im2.nii'] -v"
Inputs:
[Mandatory]
atlas_image: (a list of items which are a list of items which are an
existing file name)
The atlas image (or multimodal atlas images) assumed to be aligned
to a common image domain.
flag: -g %s...
atlas_segmentation_image: (a list of items which are an existing file
name)
The atlas segmentation images. For performing label fusion the
number of specified segmentations should be identical to the number
of atlas image sets.
flag: -l %s...
target_image: (a list of items which are a list of items which are an
existing file name)
The target image (or multimodal target images) assumed to be aligned
to a common image domain.
flag: -t %s
[Optional]
alpha: (a float, nipype default value: 0.1)
Regularization term added to matrix Mx for calculating the inverse.
Default = 0.1
flag: -a %s
args: (a unicode string)
Additional parameters to the command
flag: %s
beta: (a float, nipype default value: 2.0)
Exponent for mapping intensity difference to the joint error.
Default = 2.0
flag: -b %s
constrain_nonnegative: (a boolean, nipype default value: False)
Constrain solution to non-negative weights.
flag: -c
dimension: (3 or 2 or 4)
This option forces the image to be treated as a specified-
dimensional image. If not specified, the program tries to infer the
dimensionality from the input image.
flag: -d %d
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
exclusion_image: (a list of items which are an existing file name)
Specify an exclusion region for the given label.
exclusion_image_label: (a list of items which are a unicode string)
Specify a label for the exclusion region.
flag: -e %s
requires: exclusion_image
mask_image: (an existing file name)
If a mask image is specified, fusion is only performed in the mask
region.
flag: -x %s
num_threads: (an integer (int or long), nipype default value: 1)
Number of ITK threads to use
out_atlas_voting_weight_name_format: (a unicode string)
Optional atlas voting weight image file name format.
requires: out_label_fusion, out_intensity_fusion_name_format,
out_label_post_prob_name_format
out_intensity_fusion_name_format: (a unicode string)
Optional intensity fusion image file name format. (e.g.
"antsJointFusionIntensity_%d.nii.gz")
out_label_fusion: (a file name)
The output label fusion image.
flag: %s
out_label_post_prob_name_format: (a unicode string)
Optional label posterior probability image file name format.
requires: out_label_fusion, out_intensity_fusion_name_format
patch_metric: ('PC' or 'MSQ')
Metric to be used in determining the most similar neighborhood
patch. Options include Pearson's correlation (PC) and mean squares
(MSQ). Default = PC (Pearson correlation).
flag: -m %s
patch_radius: (a list of items which are a value of class 'int')
Patch radius for similarity measures.Default: 2x2x2
flag: -p %s
retain_atlas_voting_images: (a boolean, nipype default value: False)
Retain atlas voting images. Default = false
flag: -f
retain_label_posterior_images: (a boolean, nipype default value:
False)
Retain label posterior probability images. Requires atlas
segmentations to be specified. Default = false
flag: -r
requires: atlas_segmentation_image
search_radius: (a list of from 1 to 3 items which are any value,
nipype default value: [3, 3, 3])
Search radius for similarity measures. Default = 3x3x3. One can also
specify an image where the value at the voxel specifies the
isotropic search radius at that voxel.
flag: -s %s
verbose: (a boolean)
Verbose output.
flag: -v
Outputs:
out_atlas_voting_weight_name_format: (a unicode string)
out_intensity_fusion_name_format: (a unicode string)
out_label_fusion: (an existing file name)
out_label_post_prob_name_format: (a unicode string)
Atropos¶
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] --use-random-seed 1'
Inputs:
[Mandatory]
initialization: ('Random' or 'Otsu' or 'KMeans' or
'PriorProbabilityImages' or 'PriorLabelImage')
flag: %s
requires: number_of_tissue_classes
intensity_images: (a list of items which are an existing file name)
flag: --intensity-image %s...
mask_image: (an existing file name)
flag: --mask-image %s
number_of_tissue_classes: (an integer (int or long))
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
convergence_threshold: (a float)
requires: n_iterations
dimension: (3 or 2 or 4, nipype default value: 3)
image dimension (2, 3, or 4)
flag: --image-dimensionality %d
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
icm_use_synchronous_update: (a boolean)
flag: %s
likelihood_model: (a unicode string)
flag: --likelihood-model %s
maximum_number_of_icm_terations: (an integer (int or long))
requires: icm_use_synchronous_update
mrf_radius: (a list of items which are an integer (int or long))
requires: mrf_smoothing_factor
mrf_smoothing_factor: (a float)
flag: %s
n_iterations: (an integer (int or long))
flag: %s
num_threads: (an integer (int or long), nipype default value: 1)
Number of ITK threads to use
out_classified_image_name: (a file name)
flag: %s
output_posteriors_name_template: (a unicode string, nipype default
value: POSTERIOR_%02d.nii.gz)
posterior_formulation: (a unicode string)
flag: %s
prior_probability_images: (a list of items which are 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
use_random_seed: (a boolean, nipype default value: True)
use random seed value over constant
flag: --use-random-seed %d
Outputs:
classified_image: (an existing file name)
posteriors: (a list of items which are a file name)
BrainExtraction¶
Wraps command antsBrainExtraction.sh
Examples¶
>>> from nipype.interfaces.ants.segmentation import BrainExtraction
>>> brainextraction = BrainExtraction()
>>> brainextraction.inputs.dimension = 3
>>> brainextraction.inputs.anatomical_image ='T1.nii.gz'
>>> brainextraction.inputs.brain_template = 'study_template.nii.gz'
>>> brainextraction.inputs.brain_probability_mask ='ProbabilityMaskOfStudyTemplate.nii.gz'
>>> brainextraction.cmdline
'antsBrainExtraction.sh -a T1.nii.gz -m ProbabilityMaskOfStudyTemplate.nii.gz -e study_template.nii.gz -d 3 -s nii.gz -o highres001_'
Inputs:
[Mandatory]
anatomical_image: (an existing file name)
Structural image, typically T1. If more than one anatomical image is
specified, subsequently specified images are used during the
segmentation process. However, only the first image is used in the
registration of priors. Our suggestion would be to specify the T1 as
the first image. Anatomical template created using e.g. LPBA40 data
set with buildtemplateparallel.sh in ANTs.
flag: -a %s
brain_probability_mask: (an existing file name)
Brain probability mask created using e.g. LPBA40 data set which have
brain masks defined, and warped to anatomical template and averaged
resulting in a probability image.
flag: -m %s
brain_template: (an existing file name)
Anatomical template created using e.g. LPBA40 data set with
buildtemplateparallel.sh in ANTs.
flag: -e %s
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
debug: (a boolean)
If > 0, runs a faster version of the script. Only for testing.
Implies -u 0. Requires single thread computation for complete
reproducibility.
flag: -z 1
dimension: (3 or 2, nipype default value: 3)
image dimension (2 or 3)
flag: -d %d
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
extraction_registration_mask: (an existing file name)
Mask (defined in the template space) used during registration for
brain extraction. To limit the metric computation to a specific
region.
flag: -f %s
image_suffix: (a unicode string, nipype default value: nii.gz)
any of standard ITK formats, nii.gz is default
flag: -s %s
keep_temporary_files: (an integer (int or long))
Keep brain extraction/segmentation warps, etc (default = 0).
flag: -k %d
num_threads: (an integer (int or long), nipype default value: 1)
Number of ITK threads to use
out_prefix: (a unicode string, nipype default value: highres001_)
Prefix that is prepended to all output files (default =
highress001_)
flag: -o %s
use_floatingpoint_precision: (0 or 1)
Use floating point precision in registrations (default = 0)
flag: -q %d
use_random_seeding: (0 or 1)
Use random number generated from system clock in Atropos (default =
1)
flag: -u %d
Outputs:
BrainExtractionBrain: (an existing file name)
brain extraction image
BrainExtractionCSF: (an existing file name)
segmentation mask with only CSF
BrainExtractionGM: (an existing file name)
segmentation mask with only grey matter
BrainExtractionInitialAffine: (an existing file name)
BrainExtractionInitialAffineFixed: (an existing file name)
BrainExtractionInitialAffineMoving: (an existing file name)
BrainExtractionLaplacian: (an existing file name)
BrainExtractionMask: (an existing file name)
brain extraction mask
BrainExtractionPrior0GenericAffine: (an existing file name)
BrainExtractionPrior1InverseWarp: (an existing file name)
BrainExtractionPrior1Warp: (an existing file name)
BrainExtractionPriorWarped: (an existing file name)
BrainExtractionSegmentation: (an existing file name)
segmentation mask with CSF, GM, and WM
BrainExtractionTemplateLaplacian: (an existing file name)
BrainExtractionTmp: (an existing file name)
BrainExtractionWM: (an existing file name)
segmenration mask with only white matter
N4Corrected0: (an existing file name)
N4 bias field corrected image
N4Truncated0: (an existing file name)
CorticalThickness¶
Wraps command antsCorticalThickness.sh
Examples¶
>>> from nipype.interfaces.ants.segmentation import CorticalThickness
>>> corticalthickness = CorticalThickness()
>>> corticalthickness.inputs.dimension = 3
>>> corticalthickness.inputs.anatomical_image ='T1.nii.gz'
>>> corticalthickness.inputs.brain_template = 'study_template.nii.gz'
>>> corticalthickness.inputs.brain_probability_mask ='ProbabilityMaskOfStudyTemplate.nii.gz'
>>> corticalthickness.inputs.segmentation_priors = ['BrainSegmentationPrior01.nii.gz',
... 'BrainSegmentationPrior02.nii.gz',
... 'BrainSegmentationPrior03.nii.gz',
... 'BrainSegmentationPrior04.nii.gz']
>>> corticalthickness.inputs.t1_registration_template = 'brain_study_template.nii.gz'
>>> corticalthickness.cmdline
'antsCorticalThickness.sh -a T1.nii.gz -m ProbabilityMaskOfStudyTemplate.nii.gz -e study_template.nii.gz -d 3 -s nii.gz -o antsCT_ -p nipype_priors/BrainSegmentationPrior%02d.nii.gz -t brain_study_template.nii.gz'
Inputs:
[Mandatory]
anatomical_image: (an existing file name)
Structural *intensity* image, typically T1. If more than one
anatomical image is specified, subsequently specified images are
used during the segmentation process. However, only the first image
is used in the registration of priors. Our suggestion would be to
specify the T1 as the first image.
flag: -a %s
brain_probability_mask: (an existing file name)
brain probability mask in template space
flag: -m %s
brain_template: (an existing file name)
Anatomical *intensity* template (possibly created using a population
data set with buildtemplateparallel.sh in ANTs). This template is
*not* skull-stripped.
flag: -e %s
segmentation_priors: (a list of items which are an existing file
name)
flag: -p %s
t1_registration_template: (an existing file name)
Anatomical *intensity* template (assumed to be skull-stripped). A
common case would be where this would be the same template as
specified in the -e option which is not skull stripped.
flag: -t %s
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
b_spline_smoothing: (a boolean)
Use B-spline SyN for registrations and B-spline exponential mapping
in DiReCT.
flag: -v
cortical_label_image: (an existing file name)
Cortical ROI labels to use as a prior for ATITH.
debug: (a boolean)
If > 0, runs a faster version of the script. Only for testing.
Implies -u 0. Requires single thread computation for complete
reproducibility.
flag: -z 1
dimension: (3 or 2, nipype default value: 3)
image dimension (2 or 3)
flag: -d %d
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
extraction_registration_mask: (an existing file name)
Mask (defined in the template space) used during registration for
brain extraction.
flag: -f %s
image_suffix: (a unicode string, nipype default value: nii.gz)
any of standard ITK formats, nii.gz is default
flag: -s %s
keep_temporary_files: (an integer (int or long))
Keep brain extraction/segmentation warps, etc (default = 0).
flag: -k %d
label_propagation: (a unicode string)
Incorporate a distance prior one the posterior formulation. Should
be of the form 'label[lambda,boundaryProbability]' where label is a
value of 1,2,3,... denoting label ID. The label probability for
anything outside the current label = boundaryProbability * exp(
-lambda * distanceFromBoundary ) Intuitively, smaller lambda values
will increase the spatial capture range of the distance prior. To
apply to all label values, simply omit specifying the label, i.e. -l
[lambda,boundaryProbability].
flag: -l %s
max_iterations: (an integer (int or long))
ANTS registration max iterations (default = 100x100x70x20)
flag: -i %d
num_threads: (an integer (int or long), nipype default value: 1)
Number of ITK threads to use
out_prefix: (a unicode string, nipype default value: antsCT_)
Prefix that is prepended to all output files (default = antsCT_)
flag: -o %s
posterior_formulation: (a unicode string)
Atropos posterior formulation and whether or not to use mixture
model proportions. e.g 'Socrates[1]' (default) or 'Aristotle[1]'.
Choose the latter if you want use the distance priors (see also the
-l option for label propagation control).
flag: -b %s
prior_segmentation_weight: (a float)
Atropos spatial prior *probability* weight for the segmentation
flag: -w %f
quick_registration: (a boolean)
If = 1, use antsRegistrationSyNQuick.sh as the basis for
registration during brain extraction, brain segmentation, and
(optional) normalization to a template. Otherwise use
antsRegistrationSyN.sh (default = 0).
flag: -q 1
segmentation_iterations: (an integer (int or long))
N4 -> Atropos -> N4 iterations during segmentation (default = 3)
flag: -n %d
use_floatingpoint_precision: (0 or 1)
Use floating point precision in registrations (default = 0)
flag: -j %d
use_random_seeding: (0 or 1)
Use random number generated from system clock in Atropos (default =
1)
flag: -u %d
Outputs:
BrainExtractionMask: (an existing file name)
brain extraction mask
BrainSegmentation: (an existing file name)
brain segmentaion image
BrainSegmentationN4: (an existing file name)
N4 corrected image
BrainSegmentationPosteriors: (a list of items which are an existing
file name)
Posterior probability images
BrainVolumes: (an existing file name)
Brain volumes as text
CorticalThickness: (an existing file name)
cortical thickness file
CorticalThicknessNormedToTemplate: (an existing file name)
Normalized cortical thickness
SubjectToTemplate0GenericAffine: (an existing file name)
Template to subject inverse affine
SubjectToTemplate1Warp: (an existing file name)
Template to subject inverse warp
SubjectToTemplateLogJacobian: (an existing file name)
Template to subject log jacobian
TemplateToSubject0Warp: (an existing file name)
Template to subject warp
TemplateToSubject1GenericAffine: (an existing file name)
Template to subject affine
DenoiseImage¶
Wraps command DenoiseImage
Examples¶
>>> import copy
>>> from nipype.interfaces.ants import DenoiseImage
>>> denoise = DenoiseImage()
>>> denoise.inputs.dimension = 3
>>> denoise.inputs.input_image = 'im1.nii'
>>> denoise.cmdline
'DenoiseImage -d 3 -i im1.nii -n Gaussian -o im1_noise_corrected.nii -s 1'
>>> denoise_2 = copy.deepcopy(denoise)
>>> denoise_2.inputs.output_image = 'output_corrected_image.nii.gz'
>>> denoise_2.inputs.noise_model = 'Rician'
>>> denoise_2.inputs.shrink_factor = 2
>>> denoise_2.cmdline
'DenoiseImage -d 3 -i im1.nii -n Rician -o output_corrected_image.nii.gz -s 2'
>>> denoise_3 = DenoiseImage()
>>> denoise_3.inputs.input_image = 'im1.nii'
>>> denoise_3.inputs.save_noise = True
>>> denoise_3.cmdline
'DenoiseImage -i im1.nii -n Gaussian -o [ im1_noise_corrected.nii, im1_noise.nii ] -s 1'
Inputs:
[Mandatory]
input_image: (an existing file name)
A scalar image is expected as input for noise correction.
flag: -i %s
save_noise: (a boolean, nipype default value: False)
True if the estimated noise should be saved to file.
mutually_exclusive: noise_image
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
dimension: (2 or 3 or 4)
This option forces the image to be treated as a specified-
dimensional image. If not specified, the program tries to infer the
dimensionality from the input image.
flag: -d %d
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
noise_image: (a file name)
Filename for the estimated noise.
noise_model: ('Gaussian' or 'Rician', nipype default value: Gaussian)
Employ a Rician or Gaussian noise model.
flag: -n %s
num_threads: (an integer (int or long), nipype default value: 1)
Number of ITK threads to use
output_image: (a file name)
The output consists of the noise corrected version of the input
image.
flag: -o %s
shrink_factor: (an integer (int or long), nipype default value: 1)
Running noise correction on large images can be time consuming. To
lessen computation time, the input image can be resampled. The
shrink factor, specified as a single integer, describes this
resampling. Shrink factor = 1 is the default.
flag: -s %s
verbose: (a boolean)
Verbose output.
flag: -v
Outputs:
noise_image: (a file name)
output_image: (an existing file name)
JointFusion¶
Wraps command jointfusion
Examples¶
>>> from nipype.interfaces.ants import JointFusion
>>> at = JointFusion()
>>> at.inputs.dimension = 3
>>> at.inputs.modalities = 1
>>> at.inputs.method = 'Joint[0.1,2]'
>>> at.inputs.output_label_image ='fusion_labelimage_output.nii'
>>> at.inputs.warped_intensity_images = ['im1.nii',
... 'im2.nii',
... 'im3.nii']
>>> at.inputs.warped_label_images = ['segmentation0.nii.gz',
... 'segmentation1.nii.gz',
... 'segmentation1.nii.gz']
>>> at.inputs.target_image = 'T1.nii'
>>> at.cmdline
'jointfusion 3 1 -m Joint[0.1,2] -tg T1.nii -g im1.nii -g im2.nii -g im3.nii -l segmentation0.nii.gz -l segmentation1.nii.gz -l segmentation1.nii.gz fusion_labelimage_output.nii'
>>> at.inputs.method = 'Joint'
>>> at.inputs.alpha = 0.5
>>> at.inputs.beta = 1
>>> at.inputs.patch_radius = [3,2,1]
>>> at.inputs.search_radius = [1,2,3]
>>> at.cmdline
'jointfusion 3 1 -m Joint[0.5,1] -rp 3x2x1 -rs 1x2x3 -tg T1.nii -g im1.nii -g im2.nii -g im3.nii -l segmentation0.nii.gz -l segmentation1.nii.gz -l segmentation1.nii.gz fusion_labelimage_output.nii'
Inputs:
[Mandatory]
dimension: (3 or 2 or 4, nipype default value: 3)
image dimension (2, 3, or 4)
flag: %d, position: 0
modalities: (an integer (int or long))
Number of modalities or features
flag: %d, position: 1
output_label_image: (a file name)
Output fusion label map image
flag: %s, position: -1
target_image: (a list of items which are an existing file name)
Target image(s)
flag: -tg %s...
warped_intensity_images: (a list of items which are an existing file
name)
Warped atlas images
flag: -g %s...
warped_label_images: (a list of items which are an existing file
name)
Warped atlas segmentations
flag: -l %s...
[Optional]
alpha: (a float, nipype default value: 0.0)
Regularization term added to matrix Mx for inverse
requires: method
args: (a unicode string)
Additional parameters to the command
flag: %s
atlas_group_id: (a list of items which are a value of class 'int')
Assign a group ID for each atlas
flag: -gp %d...
atlas_group_weights: (a list of items which are a value of class
'int')
Assign the voting weights to each atlas group
flag: -gpw %d...
beta: (an integer (int or long), nipype default value: 0)
Exponent for mapping intensity difference to joint error
requires: method
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
exclusion_region: (an existing file name)
Specify an exclusion region for the given label.
flag: -x %s
method: (a unicode string, nipype default value: )
Select voting method. Options: Joint (Joint Label Fusion). May be
followed by optional parameters in brackets, e.g., -m Joint[0.1,2]
flag: -m %s
num_threads: (an integer (int or long), nipype default value: 1)
Number of ITK threads to use
patch_radius: (a list of items which are a value of class 'int')
Patch radius for similarity measures, scalar or vector. Default:
2x2x2
flag: -rp %s
search_radius: (a list of items which are a value of class 'int')
Local search radius. Default: 3x3x3
flag: -rs %s
Outputs:
output_label_image: (an existing file name)
KellyKapowski¶
Wraps command KellyKapowski
Nipype Interface to ANTs’ KellyKapowski, also known as DiReCT.
DiReCT is a registration based estimate of cortical thickness. It was published in S. R. Das, B. B. Avants, M. Grossman, and J. C. Gee, Registration based cortical thickness measurement, Neuroimage 2009, 45:867–879.
Examples¶
>>> from nipype.interfaces.ants.segmentation import KellyKapowski
>>> kk = KellyKapowski()
>>> kk.inputs.dimension = 3
>>> kk.inputs.segmentation_image = "segmentation0.nii.gz"
>>> kk.inputs.convergence = "[45,0.0,10]"
>>> kk.inputs.thickness_prior_estimate = 10
>>> kk.cmdline
'KellyKapowski --convergence "[45,0.0,10]" --output "[segmentation0_cortical_thickness.nii.gz,segmentation0_warped_white_matter.nii.gz]" --image-dimensionality 3 --gradient-step 0.025000 --maximum-number-of-invert-displacement-field-iterations 20 --number-of-integration-points 10 --segmentation-image "[segmentation0.nii.gz,2,3]" --smoothing-variance 1.000000 --smoothing-velocity-field-parameter 1.500000 --thickness-prior-estimate 10.000000'
Inputs:
[Mandatory]
segmentation_image: (an existing file name)
A segmentation image must be supplied labeling the gray and white
matters. Default values = 2 and 3, respectively.
flag: --segmentation-image "%s"
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
convergence: (a unicode string, nipype default value: )
Convergence is determined by fitting a line to the normalized energy
profile of the last N iterations (where N is specified by the window
size) and determining the slope which is then compared with the
convergence threshold.
flag: --convergence "%s"
cortical_thickness: (a file name)
Filename for the cortical thickness.
flag: --output "%s"
dimension: (3 or 2, nipype default value: 3)
image dimension (2 or 3)
flag: --image-dimensionality %d
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
gradient_step: (a float, nipype default value: 0.025)
Gradient step size for the optimization.
flag: --gradient-step %f
gray_matter_label: (an integer (int or long), nipype default value:
2)
The label value for the gray matter label in the segmentation_image.
gray_matter_prob_image: (an existing file name)
In addition to the segmentation image, a gray matter probability
image can be used. If no such image is supplied, one is created
using the segmentation image and a variance of 1.0 mm.
flag: --gray-matter-probability-image "%s"
max_invert_displacement_field_iters: (an integer (int or long),
nipype default value: 20)
Maximum number of iterations for estimating the invertdisplacement
field.
flag: --maximum-number-of-invert-displacement-field-iterations %d
num_threads: (an integer (int or long), nipype default value: 1)
Number of ITK threads to use
number_integration_points: (an integer (int or long), nipype default
value: 10)
Number of compositions of the diffeomorphism per iteration.
flag: --number-of-integration-points %d
smoothing_variance: (a float, nipype default value: 1.0)
Defines the Gaussian smoothing of the hit and total images.
flag: --smoothing-variance %f
smoothing_velocity_field: (a float, nipype default value: 1.5)
Defines the Gaussian smoothing of the velocity field (default =
1.5). If the b-spline smoothing option is chosen, then this defines
the isotropic mesh spacing for the smoothing spline (default = 15).
flag: --smoothing-velocity-field-parameter %f
thickness_prior_estimate: (a float, nipype default value: 10)
Provides a prior constraint on the final thickness measurement in
mm.
flag: --thickness-prior-estimate %f
thickness_prior_image: (an existing file name)
An image containing spatially varying prior thickness values.
flag: --thickness-prior-image "%s"
use_bspline_smoothing: (a boolean)
Sets the option for B-spline smoothing of the velocity field.
flag: --use-bspline-smoothing 1
warped_white_matter: (a file name)
Filename for the warped white matter file.
white_matter_label: (an integer (int or long), nipype default value:
3)
The label value for the white matter label in the
segmentation_image.
white_matter_prob_image: (an existing file name)
In addition to the segmentation image, a white matter probability
image can be used. If no such image is supplied, one is created
using the segmentation image and a variance of 1.0 mm.
flag: --white-matter-probability-image "%s"
Outputs:
cortical_thickness: (a file name)
A thickness map defined in the segmented gray matter.
warped_white_matter: (a file name)
A warped white matter image.
References:: BibTeX(‘@book{Das2009867,author={Sandhitsu R. Das and Brian B. Avants and Murray Grossman and James C. Gee},title={Registration based cortical thickness measurement.},journal={NeuroImage},volume={45},number={37},pages={867–879},year={2009},issn={1053-8119},url={http://www.sciencedirect.com/science/article/pii/S1053811908012780},doi={http://dx.doi.org/10.1016/j.neuroimage.2008.12.016}}’, key=’Das2009867’)
LaplacianThickness¶
Wraps command LaplacianThickness
Calculates the cortical thickness from an anatomical image
Examples¶
>>> from nipype.interfaces.ants import LaplacianThickness
>>> cort_thick = LaplacianThickness()
>>> cort_thick.inputs.input_wm = 'white_matter.nii.gz'
>>> cort_thick.inputs.input_gm = 'gray_matter.nii.gz'
>>> cort_thick.cmdline
'LaplacianThickness white_matter.nii.gz gray_matter.nii.gz white_matter_thickness.nii.gz'
>>> cort_thick.inputs.output_image = 'output_thickness.nii.gz'
>>> cort_thick.cmdline
'LaplacianThickness white_matter.nii.gz gray_matter.nii.gz output_thickness.nii.gz'
Inputs:
[Mandatory]
input_gm: (a file name)
gray matter segmentation image
flag: %s, position: 2
input_wm: (a file name)
white matter segmentation image
flag: %s, position: 1
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
dT: (a float)
flag: dT=%d, position: 6
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
num_threads: (an integer (int or long), nipype default value: 1)
Number of ITK threads to use
opt_tolerance: (a float)
flag: optional-laplacian-tolerance=%d, position: 8
output_image: (a file name)
name of output file
flag: %s, position: 3
prior_thickness: (a float)
flag: priorthickval=%d, position: 5
smooth_param: (a float)
flag: smoothparam=%d, position: 4
sulcus_prior: (a boolean)
flag: use-sulcus-prior, position: 7
Outputs:
output_image: (an existing file name)
Cortical thickness
N4BiasFieldCorrection¶
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 [Tustison2010].
| [Tustison2010] | N. Tustison et al., N4ITK: Improved N3 Bias Correction, IEEE Transactions on Medical Imaging, 29(6):1310-1320, June 2010. |
Examples¶
>>> import copy
>>> 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.cmdline
'N4BiasFieldCorrection --bspline-fitting [ 300 ] -d 3 --input-image structural.nii --convergence [ 50x50x30x20 ] --output structural_corrected.nii --shrink-factor 3'
>>> n4_2 = copy.deepcopy(n4)
>>> n4_2.inputs.convergence_threshold = 1e-6
>>> n4_2.cmdline
'N4BiasFieldCorrection --bspline-fitting [ 300 ] -d 3 --input-image structural.nii --convergence [ 50x50x30x20, 1e-06 ] --output structural_corrected.nii --shrink-factor 3'
>>> n4_3 = copy.deepcopy(n4_2)
>>> n4_3.inputs.bspline_order = 5
>>> n4_3.cmdline
'N4BiasFieldCorrection --bspline-fitting [ 300, 5 ] -d 3 --input-image structural.nii --convergence [ 50x50x30x20, 1e-06 ] --output structural_corrected.nii --shrink-factor 3'
>>> n4_4 = N4BiasFieldCorrection()
>>> n4_4.inputs.input_image = 'structural.nii'
>>> n4_4.inputs.save_bias = True
>>> n4_4.inputs.dimension = 3
>>> n4_4.cmdline
'N4BiasFieldCorrection -d 3 --input-image structural.nii --output [ structural_corrected.nii, structural_bias.nii ]'
Inputs:
[Mandatory]
copy_header: (a boolean, nipype default value: False)
copy headers of the original image into the output (corrected) file
input_image: (a file name)
input for bias correction. Negative values or values close to zero
should be processed prior to correction
flag: --input-image %s
save_bias: (a boolean, nipype default value: False)
True if the estimated bias should be saved to file.
mutually_exclusive: bias_image
[Optional]
args: (a unicode string)
Additional parameters to the command
flag: %s
bias_image: (a file name)
Filename for the estimated bias.
bspline_fitting_distance: (a float)
flag: --bspline-fitting %s
bspline_order: (an integer (int or long))
requires: bspline_fitting_distance
convergence_threshold: (a float)
requires: n_iterations
dimension: (3 or 2 or 4, nipype default value: 3)
image dimension (2, 3 or 4)
flag: -d %d
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
mask_image: (a file name)
image to specify region to perform final bias correction in
flag: --mask-image %s
n_iterations: (a list of items which are an integer (int or long))
flag: --convergence %s
num_threads: (an integer (int or long), nipype default value: 1)
Number of ITK threads to use
output_image: (a unicode string)
output file name
flag: --output %s
shrink_factor: (an integer (int or long))
flag: --shrink-factor %d
weight_image: (a file name)
image for relative weighting (e.g. probability map of the white
matter) of voxels during the B-spline fitting.
flag: --weight-image %s
Outputs:
bias_image: (an existing file name)
Estimated bias
output_image: (an existing file name)
Warped image