/
segmentation.py
1888 lines (1732 loc) · 70.1 KB
/
segmentation.py
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# -*- coding: utf-8 -*-
"""The ants module provides basic functions for interfacing with ants functions.
"""
import os
from ...external.due import BibTeX
from ...utils.filemanip import split_filename, copyfile, which, fname_presuffix
from ..base import TraitedSpec, File, traits, InputMultiPath, OutputMultiPath, isdefined
from .base import ANTSCommand, ANTSCommandInputSpec
class AtroposInputSpec(ANTSCommandInputSpec):
dimension = traits.Enum(
3,
2,
4,
argstr="--image-dimensionality %d",
usedefault=True,
desc="image dimension (2, 3, or 4)",
)
intensity_images = InputMultiPath(
File(exists=True), argstr="--intensity-image %s...", mandatory=True
)
mask_image = File(exists=True, argstr="--mask-image %s", mandatory=True)
initialization = traits.Enum(
"Random",
"Otsu",
"KMeans",
"PriorProbabilityImages",
"PriorLabelImage",
argstr="%s",
requires=["number_of_tissue_classes"],
mandatory=True,
)
prior_probability_images = InputMultiPath(File(exists=True))
number_of_tissue_classes = traits.Int(mandatory=True)
prior_weighting = traits.Float()
prior_probability_threshold = traits.Float(requires=["prior_weighting"])
likelihood_model = traits.Str(argstr="--likelihood-model %s")
mrf_smoothing_factor = traits.Float(argstr="%s")
mrf_radius = traits.List(traits.Int(), requires=["mrf_smoothing_factor"])
icm_use_synchronous_update = traits.Bool(argstr="%s")
maximum_number_of_icm_terations = traits.Int(
requires=["icm_use_synchronous_update"]
)
n_iterations = traits.Int(argstr="%s")
convergence_threshold = traits.Float(requires=["n_iterations"])
posterior_formulation = traits.Str(argstr="%s")
use_random_seed = traits.Bool(
True,
argstr="--use-random-seed %d",
desc="use random seed value over constant",
usedefault=True,
)
use_mixture_model_proportions = traits.Bool(requires=["posterior_formulation"])
out_classified_image_name = File(argstr="%s", genfile=True, hash_files=False)
save_posteriors = traits.Bool()
output_posteriors_name_template = traits.Str(
"POSTERIOR_%02d.nii.gz", usedefault=True
)
class AtroposOutputSpec(TraitedSpec):
classified_image = File(exists=True)
posteriors = OutputMultiPath(File(exist=True))
class Atropos(ANTSCommand):
"""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'
"""
input_spec = AtroposInputSpec
output_spec = AtroposOutputSpec
_cmd = "Atropos"
def _format_arg(self, opt, spec, val):
if opt == "initialization":
retval = "--initialization %s[%d" % (
val,
self.inputs.number_of_tissue_classes,
)
if val == "PriorProbabilityImages":
_, _, ext = split_filename(self.inputs.prior_probability_images[0])
retval += (
",priors/priorProbImages%02d"
+ ext
+ ",%g" % self.inputs.prior_weighting
)
if isdefined(self.inputs.prior_probability_threshold):
retval += ",%g" % self.inputs.prior_probability_threshold
return retval + "]"
if opt == "mrf_smoothing_factor":
retval = "--mrf [%g" % val
if isdefined(self.inputs.mrf_radius):
retval += ",%s" % self._format_xarray(
[str(s) for s in self.inputs.mrf_radius]
)
return retval + "]"
if opt == "icm_use_synchronous_update":
retval = "--icm [%d" % val
if isdefined(self.inputs.maximum_number_of_icm_terations):
retval += ",%g" % self.inputs.maximum_number_of_icm_terations
return retval + "]"
if opt == "n_iterations":
retval = "--convergence [%d" % val
if isdefined(self.inputs.convergence_threshold):
retval += ",%g" % self.inputs.convergence_threshold
return retval + "]"
if opt == "posterior_formulation":
retval = "--posterior-formulation %s" % val
if isdefined(self.inputs.use_mixture_model_proportions):
retval += "[%d]" % self.inputs.use_mixture_model_proportions
return retval
if opt == "out_classified_image_name":
retval = "--output [%s" % val
if isdefined(self.inputs.save_posteriors):
retval += ",%s" % self.inputs.output_posteriors_name_template
return retval + "]"
return super(Atropos, self)._format_arg(opt, spec, val)
def _run_interface(self, runtime, correct_return_codes=[0]):
if self.inputs.initialization == "PriorProbabilityImages":
priors_directory = os.path.join(os.getcwd(), "priors")
if not os.path.exists(priors_directory):
os.makedirs(priors_directory)
_, _, ext = split_filename(self.inputs.prior_probability_images[0])
for i, f in enumerate(self.inputs.prior_probability_images):
target = os.path.join(
priors_directory, "priorProbImages%02d" % (i + 1) + ext
)
if not (
os.path.exists(target)
and os.path.realpath(target) == os.path.abspath(f)
):
copyfile(
os.path.abspath(f),
os.path.join(
priors_directory, "priorProbImages%02d" % (i + 1) + ext
),
)
runtime = super(Atropos, self)._run_interface(runtime)
return runtime
def _gen_filename(self, name):
if name == "out_classified_image_name":
output = self.inputs.out_classified_image_name
if not isdefined(output):
_, name, ext = split_filename(self.inputs.intensity_images[0])
output = name + "_labeled" + ext
return output
return None
def _list_outputs(self):
outputs = self._outputs().get()
outputs["classified_image"] = os.path.abspath(
self._gen_filename("out_classified_image_name")
)
if isdefined(self.inputs.save_posteriors) and self.inputs.save_posteriors:
outputs["posteriors"] = []
for i in range(self.inputs.number_of_tissue_classes):
outputs["posteriors"].append(
os.path.abspath(
self.inputs.output_posteriors_name_template % (i + 1)
)
)
return outputs
class LaplacianThicknessInputSpec(ANTSCommandInputSpec):
input_wm = File(
argstr="%s",
mandatory=True,
copyfile=True,
desc="white matter segmentation image",
position=1,
)
input_gm = File(
argstr="%s",
mandatory=True,
copyfile=True,
desc="gray matter segmentation image",
position=2,
)
output_image = File(
desc="name of output file",
argstr="%s",
position=3,
name_source=["input_wm"],
name_template="%s_thickness",
keep_extension=True,
hash_files=False,
)
smooth_param = traits.Float(
argstr="%s",
desc="Sigma of the Laplacian Recursive Image Filter (defaults to 1)",
position=4,
)
prior_thickness = traits.Float(
argstr="%s",
desc="Prior thickness (defaults to 500)",
requires=["smooth_param"],
position=5,
)
dT = traits.Float(
argstr="%s",
desc="Time delta used during integration (defaults to 0.01)",
requires=["prior_thickness"],
position=6,
)
sulcus_prior = traits.Float(
argstr="%s",
desc="Positive floating point number for sulcus prior. "
"Authors said that 0.15 might be a reasonable value",
requires=["dT"],
position=7,
)
tolerance = traits.Float(
argstr="%s",
desc="Tolerance to reach during optimization (defaults to 0.001)",
requires=["sulcus_prior"],
position=8,
)
class LaplacianThicknessOutputSpec(TraitedSpec):
output_image = File(exists=True, desc="Cortical thickness")
class LaplacianThickness(ANTSCommand):
"""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'
"""
_cmd = "LaplacianThickness"
input_spec = LaplacianThicknessInputSpec
output_spec = LaplacianThicknessOutputSpec
class N4BiasFieldCorrectionInputSpec(ANTSCommandInputSpec):
dimension = traits.Enum(
3, 2, 4, argstr="-d %d", usedefault=True, desc="image dimension (2, 3 or 4)"
)
input_image = File(
argstr="--input-image %s",
mandatory=True,
desc=(
"input for bias correction. Negative values or values close to "
"zero should be processed prior to correction"
),
)
mask_image = File(
argstr="--mask-image %s",
desc=("image to specify region to perform final bias correction in"),
)
weight_image = File(
argstr="--weight-image %s",
desc=(
"image for relative weighting (e.g. probability map of the white "
"matter) of voxels during the B-spline fitting. "
),
)
output_image = traits.Str(
argstr="--output %s",
desc="output file name",
name_source=["input_image"],
name_template="%s_corrected",
keep_extension=True,
hash_files=False,
)
bspline_fitting_distance = traits.Float(argstr="--bspline-fitting %s")
bspline_order = traits.Int(requires=["bspline_fitting_distance"])
shrink_factor = traits.Int(argstr="--shrink-factor %d")
n_iterations = traits.List(traits.Int(), argstr="--convergence %s")
convergence_threshold = traits.Float(requires=["n_iterations"])
save_bias = traits.Bool(
False,
mandatory=True,
usedefault=True,
desc=("True if the estimated bias should be saved to file."),
xor=["bias_image"],
)
bias_image = File(desc="Filename for the estimated bias.", hash_files=False)
copy_header = traits.Bool(
False,
mandatory=True,
usedefault=True,
desc="copy headers of the original image into the output (corrected) file",
)
rescale_intensities = traits.Bool(
False,
usedefault=True,
argstr="-r",
min_ver="2.1.0",
desc="""\
[NOTE: Only ANTs>=2.1.0]
At each iteration, a new intensity mapping is calculated and applied but there
is nothing which constrains the new intensity range to be within certain values.
The result is that the range can "drift" from the original at each iteration.
This option rescales to the [min,max] range of the original image intensities
within the user-specified mask.""",
)
histogram_sharpening = traits.Tuple(
(0.15, 0.01, 200),
traits.Float,
traits.Float,
traits.Int,
argstr="--histogram-sharpening [%g,%g,%d]",
desc="""\
Three-values tuple of histogram sharpening parameters \
(FWHM, wienerNose, numberOfHistogramBins).
These options describe the histogram sharpening parameters, i.e. the \
deconvolution step parameters described in the original N3 algorithm.
The default values have been shown to work fairly well.""",
)
class N4BiasFieldCorrectionOutputSpec(TraitedSpec):
output_image = File(exists=True, desc="Warped image")
bias_image = File(exists=True, desc="Estimated bias")
class N4BiasFieldCorrection(ANTSCommand):
"""
Bias field correction.
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 ]'
>>> n4_5 = N4BiasFieldCorrection()
>>> n4_5.inputs.input_image = 'structural.nii'
>>> n4_5.inputs.dimension = 3
>>> n4_5.inputs.histogram_sharpening = (0.12, 0.02, 200)
>>> n4_5.cmdline
'N4BiasFieldCorrection -d 3 --histogram-sharpening [0.12,0.02,200] \
--input-image structural.nii --output structural_corrected.nii'
"""
_cmd = "N4BiasFieldCorrection"
input_spec = N4BiasFieldCorrectionInputSpec
output_spec = N4BiasFieldCorrectionOutputSpec
def __init__(self, *args, **kwargs):
"""Instantiate the N4BiasFieldCorrection interface."""
self._out_bias_file = None
super(N4BiasFieldCorrection, self).__init__(*args, **kwargs)
def _format_arg(self, name, trait_spec, value):
if name == "output_image" and self._out_bias_file:
newval = "[ %s, %s ]" % (value, self._out_bias_file)
return trait_spec.argstr % newval
if name == "bspline_fitting_distance":
if isdefined(self.inputs.bspline_order):
newval = "[ %g, %d ]" % (value, self.inputs.bspline_order)
else:
newval = "[ %g ]" % value
return trait_spec.argstr % newval
if name == "n_iterations":
if isdefined(self.inputs.convergence_threshold):
newval = "[ %s, %g ]" % (
self._format_xarray([str(elt) for elt in value]),
self.inputs.convergence_threshold,
)
else:
newval = "[ %s ]" % self._format_xarray([str(elt) for elt in value])
return trait_spec.argstr % newval
return super(N4BiasFieldCorrection, self)._format_arg(name, trait_spec, value)
def _parse_inputs(self, skip=None):
skip = (skip or []) + ["save_bias", "bias_image"]
self._out_bias_file = None
if self.inputs.save_bias or isdefined(self.inputs.bias_image):
bias_image = self.inputs.bias_image
if not isdefined(bias_image):
bias_image = fname_presuffix(
os.path.basename(self.inputs.input_image), suffix="_bias"
)
self._out_bias_file = bias_image
return super(N4BiasFieldCorrection, self)._parse_inputs(skip=skip)
def _list_outputs(self):
outputs = super(N4BiasFieldCorrection, self)._list_outputs()
# Fix headers
if self.inputs.copy_header:
self._copy_header(outputs["output_image"])
if self._out_bias_file:
outputs["bias_image"] = os.path.abspath(self._out_bias_file)
if self.inputs.copy_header:
self._copy_header(outputs["bias_image"])
return outputs
def _copy_header(self, fname):
"""Copy header from input image to an output image."""
import nibabel as nb
in_img = nb.load(self.inputs.input_image)
out_img = nb.load(fname, mmap=False)
new_img = out_img.__class__(out_img.get_fdata(), in_img.affine, in_img.header)
new_img.set_data_dtype(out_img.get_data_dtype())
new_img.to_filename(fname)
class CorticalThicknessInputSpec(ANTSCommandInputSpec):
dimension = traits.Enum(
3, 2, argstr="-d %d", usedefault=True, desc="image dimension (2 or 3)"
)
anatomical_image = File(
exists=True,
argstr="-a %s",
desc=(
"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."
),
mandatory=True,
)
brain_template = File(
exists=True,
argstr="-e %s",
desc=(
"Anatomical *intensity* template (possibly created using a"
" population data set with buildtemplateparallel.sh in ANTs)."
" This template is *not* skull-stripped."
),
mandatory=True,
)
brain_probability_mask = File(
exists=True,
argstr="-m %s",
desc="brain probability mask in template space",
copyfile=False,
mandatory=True,
)
segmentation_priors = InputMultiPath(
File(exists=True), argstr="-p %s", mandatory=True
)
out_prefix = traits.Str(
"antsCT_",
argstr="-o %s",
usedefault=True,
desc=("Prefix that is prepended to all output files"),
)
image_suffix = traits.Str(
"nii.gz",
desc=("any of standard ITK formats, nii.gz is default"),
argstr="-s %s",
usedefault=True,
)
t1_registration_template = File(
exists=True,
desc=(
"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."
),
argstr="-t %s",
mandatory=True,
)
extraction_registration_mask = File(
exists=True,
argstr="-f %s",
desc=(
"Mask (defined in the template space) used during"
" registration for brain extraction."
),
)
keep_temporary_files = traits.Int(
argstr="-k %d",
desc="Keep brain extraction/segmentation warps, etc (default = 0).",
)
max_iterations = traits.Int(
argstr="-i %d",
desc=("ANTS registration max iterations (default = 100x100x70x20)"),
)
prior_segmentation_weight = traits.Float(
argstr="-w %f",
desc=("Atropos spatial prior *probability* weight for the segmentation"),
)
segmentation_iterations = traits.Int(
argstr="-n %d",
desc=("N4 -> Atropos -> N4 iterations during segmentation (default = 3)"),
)
posterior_formulation = traits.Str(
argstr="-b %s",
desc=(
"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)."
),
)
use_floatingpoint_precision = traits.Enum(
0,
1,
argstr="-j %d",
desc=("Use floating point precision in registrations (default = 0)"),
)
use_random_seeding = traits.Enum(
0,
1,
argstr="-u %d",
desc=("Use random number generated from system clock in Atropos (default = 1)"),
)
b_spline_smoothing = traits.Bool(
argstr="-v",
desc=(
"Use B-spline SyN for registrations and B-spline"
" exponential mapping in DiReCT."
),
)
cortical_label_image = File(
exists=True, desc="Cortical ROI labels to use as a prior for ATITH."
)
label_propagation = traits.Str(
argstr="-l %s",
desc=(
"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]."
),
)
quick_registration = traits.Bool(
argstr="-q 1",
desc=(
"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)."
),
)
debug = traits.Bool(
argstr="-z 1",
desc=(
"If > 0, runs a faster version of the script."
" Only for testing. Implies -u 0."
" Requires single thread computation for complete reproducibility."
),
)
class CorticalThicknessOutputSpec(TraitedSpec):
BrainExtractionMask = File(exists=True, desc="brain extraction mask")
ExtractedBrainN4 = File(exists=True, desc="extracted brain from N4 image")
BrainSegmentation = File(exists=True, desc="brain segmentaion image")
BrainSegmentationN4 = File(exists=True, desc="N4 corrected image")
BrainSegmentationPosteriors = OutputMultiPath(
File(exists=True), desc="Posterior probability images"
)
CorticalThickness = File(exists=True, desc="cortical thickness file")
TemplateToSubject1GenericAffine = File(
exists=True, desc="Template to subject affine"
)
TemplateToSubject0Warp = File(exists=True, desc="Template to subject warp")
SubjectToTemplate1Warp = File(exists=True, desc="Template to subject inverse warp")
SubjectToTemplate0GenericAffine = File(
exists=True, desc="Template to subject inverse affine"
)
SubjectToTemplateLogJacobian = File(
exists=True, desc="Template to subject log jacobian"
)
CorticalThicknessNormedToTemplate = File(
exists=True, desc="Normalized cortical thickness"
)
BrainVolumes = File(exists=True, desc="Brain volumes as text")
class CorticalThickness(ANTSCommand):
"""
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'
"""
input_spec = CorticalThicknessInputSpec
output_spec = CorticalThicknessOutputSpec
_cmd = "antsCorticalThickness.sh"
def _format_arg(self, opt, spec, val):
if opt == "anatomical_image":
retval = "-a %s" % val
return retval
if opt == "brain_template":
retval = "-e %s" % val
return retval
if opt == "brain_probability_mask":
retval = "-m %s" % val
return retval
if opt == "out_prefix":
retval = "-o %s" % val
return retval
if opt == "t1_registration_template":
retval = "-t %s" % val
return retval
if opt == "segmentation_priors":
_, _, ext = split_filename(self.inputs.segmentation_priors[0])
retval = "-p nipype_priors/BrainSegmentationPrior%02d" + ext
return retval
return super(CorticalThickness, self)._format_arg(opt, spec, val)
def _run_interface(self, runtime, correct_return_codes=[0]):
priors_directory = os.path.join(os.getcwd(), "nipype_priors")
if not os.path.exists(priors_directory):
os.makedirs(priors_directory)
_, _, ext = split_filename(self.inputs.segmentation_priors[0])
for i, f in enumerate(self.inputs.segmentation_priors):
target = os.path.join(
priors_directory, "BrainSegmentationPrior%02d" % (i + 1) + ext
)
if not (
os.path.exists(target)
and os.path.realpath(target) == os.path.abspath(f)
):
copyfile(os.path.abspath(f), target)
runtime = super(CorticalThickness, self)._run_interface(runtime)
return runtime
def _list_outputs(self):
outputs = self._outputs().get()
outputs["BrainExtractionMask"] = os.path.join(
os.getcwd(),
self.inputs.out_prefix + "BrainExtractionMask." + self.inputs.image_suffix,
)
outputs["ExtractedBrainN4"] = os.path.join(
os.getcwd(),
self.inputs.out_prefix + "ExtractedBrain0N4." + self.inputs.image_suffix,
)
outputs["BrainSegmentation"] = os.path.join(
os.getcwd(),
self.inputs.out_prefix + "BrainSegmentation." + self.inputs.image_suffix,
)
outputs["BrainSegmentationN4"] = os.path.join(
os.getcwd(),
self.inputs.out_prefix + "BrainSegmentation0N4." + self.inputs.image_suffix,
)
posteriors = []
for i in range(len(self.inputs.segmentation_priors)):
posteriors.append(
os.path.join(
os.getcwd(),
self.inputs.out_prefix
+ "BrainSegmentationPosteriors%02d." % (i + 1)
+ self.inputs.image_suffix,
)
)
outputs["BrainSegmentationPosteriors"] = posteriors
outputs["CorticalThickness"] = os.path.join(
os.getcwd(),
self.inputs.out_prefix + "CorticalThickness." + self.inputs.image_suffix,
)
outputs["TemplateToSubject1GenericAffine"] = os.path.join(
os.getcwd(), self.inputs.out_prefix + "TemplateToSubject1GenericAffine.mat"
)
outputs["TemplateToSubject0Warp"] = os.path.join(
os.getcwd(),
self.inputs.out_prefix
+ "TemplateToSubject0Warp."
+ self.inputs.image_suffix,
)
outputs["SubjectToTemplate1Warp"] = os.path.join(
os.getcwd(),
self.inputs.out_prefix
+ "SubjectToTemplate1Warp."
+ self.inputs.image_suffix,
)
outputs["SubjectToTemplate0GenericAffine"] = os.path.join(
os.getcwd(), self.inputs.out_prefix + "SubjectToTemplate0GenericAffine.mat"
)
outputs["SubjectToTemplateLogJacobian"] = os.path.join(
os.getcwd(),
self.inputs.out_prefix
+ "SubjectToTemplateLogJacobian."
+ self.inputs.image_suffix,
)
outputs["CorticalThicknessNormedToTemplate"] = os.path.join(
os.getcwd(),
self.inputs.out_prefix + "CorticalThickness." + self.inputs.image_suffix,
)
outputs["BrainVolumes"] = os.path.join(
os.getcwd(), self.inputs.out_prefix + "brainvols.csv"
)
return outputs
class BrainExtractionInputSpec(ANTSCommandInputSpec):
dimension = traits.Enum(
3, 2, argstr="-d %d", usedefault=True, desc="image dimension (2 or 3)"
)
anatomical_image = File(
exists=True,
argstr="-a %s",
desc=(
"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."
),
mandatory=True,
)
brain_template = File(
exists=True,
argstr="-e %s",
desc=(
"Anatomical template created using e.g. LPBA40 data set with"
" buildtemplateparallel.sh in ANTs."
),
mandatory=True,
)
brain_probability_mask = File(
exists=True,
argstr="-m %s",
desc=(
"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."
),
copyfile=False,
mandatory=True,
)
out_prefix = traits.Str(
"highres001_",
argstr="-o %s",
usedefault=True,
desc=("Prefix that is prepended to all output files"),
)
extraction_registration_mask = File(
exists=True,
argstr="-f %s",
desc=(
"Mask (defined in the template space) used during"
" registration for brain extraction."
" To limit the metric computation to a specific region."
),
)
image_suffix = traits.Str(
"nii.gz",
desc=("any of standard ITK formats, nii.gz is default"),
argstr="-s %s",
usedefault=True,
)
use_random_seeding = traits.Enum(
0,
1,
argstr="-u %d",
desc=("Use random number generated from system clock in Atropos (default = 1)"),
)
keep_temporary_files = traits.Int(
argstr="-k %d",
desc="Keep brain extraction/segmentation warps, etc (default = 0).",
)
use_floatingpoint_precision = traits.Enum(
0,
1,
argstr="-q %d",
desc=("Use floating point precision in registrations (default = 0)"),
)
debug = traits.Bool(
argstr="-z 1",
desc=(
"If > 0, runs a faster version of the script."
" Only for testing. Implies -u 0."
" Requires single thread computation for complete reproducibility."
),
)
class BrainExtractionOutputSpec(TraitedSpec):
BrainExtractionMask = File(exists=True, desc="brain extraction mask")
BrainExtractionBrain = File(exists=True, desc="brain extraction image")
BrainExtractionCSF = File(exists=True, desc="segmentation mask with only CSF")
BrainExtractionGM = File(
exists=True, desc="segmentation mask with only grey matter"
)
BrainExtractionInitialAffine = File(exists=True, desc="")
BrainExtractionInitialAffineFixed = File(exists=True, desc="")
BrainExtractionInitialAffineMoving = File(exists=True, desc="")
BrainExtractionLaplacian = File(exists=True, desc="")
BrainExtractionPrior0GenericAffine = File(exists=True, desc="")
BrainExtractionPrior1InverseWarp = File(exists=True, desc="")
BrainExtractionPrior1Warp = File(exists=True, desc="")
BrainExtractionPriorWarped = File(exists=True, desc="")
BrainExtractionSegmentation = File(
exists=True, desc="segmentation mask with CSF, GM, and WM"
)
BrainExtractionTemplateLaplacian = File(exists=True, desc="")
BrainExtractionTmp = File(exists=True, desc="")
BrainExtractionWM = File(
exists=True, desc="segmenration mask with only white matter"
)
N4Corrected0 = File(exists=True, desc="N4 bias field corrected image")
N4Truncated0 = File(exists=True, desc="")
class BrainExtraction(ANTSCommand):
"""
Atlas-based brain extraction.
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_'
"""
input_spec = BrainExtractionInputSpec
output_spec = BrainExtractionOutputSpec
_cmd = "antsBrainExtraction.sh"
def _run_interface(self, runtime, correct_return_codes=(0,)):
# antsBrainExtraction.sh requires ANTSPATH to be defined
out_environ = self._get_environ()
ants_path = out_environ.get("ANTSPATH", None) or os.getenv("ANTSPATH", None)
if ants_path is None:
# Check for antsRegistration, which is under bin/ (the $ANTSPATH) instead of
# checking for antsBrainExtraction.sh which is under script/
cmd_path = which("antsRegistration", env=runtime.environ)
if not cmd_path:
raise RuntimeError(
'The environment variable $ANTSPATH is not defined in host "%s", '
"and Nipype could not determine it automatically."
% runtime.hostname
)
ants_path = os.path.dirname(cmd_path)
self.inputs.environ.update({"ANTSPATH": ants_path})
runtime.environ.update({"ANTSPATH": ants_path})
runtime = super(BrainExtraction, self)._run_interface(runtime)
# Still, double-check if it didn't found N4
if "we cant find" in runtime.stdout:
for line in runtime.stdout.split("\n"):
if line.strip().startswith("we cant find"):
tool = line.strip().replace("we cant find the", "").split(" ")[0]
break
errmsg = (
'antsBrainExtraction.sh requires "%s" to be found in $ANTSPATH '
'($ANTSPATH="%s").'
) % (tool, ants_path)
if runtime.stderr is None:
runtime.stderr = errmsg
else:
runtime.stderr += "\n" + errmsg
runtime.returncode = 1
self.raise_exception(runtime)
return runtime
def _list_outputs(self):
outputs = self._outputs().get()
outputs["BrainExtractionMask"] = os.path.join(
os.getcwd(),
self.inputs.out_prefix + "BrainExtractionMask." + self.inputs.image_suffix,
)
outputs["BrainExtractionBrain"] = os.path.join(
os.getcwd(),
self.inputs.out_prefix + "BrainExtractionBrain." + self.inputs.image_suffix,
)
if (
isdefined(self.inputs.keep_temporary_files)
and self.inputs.keep_temporary_files != 0