def init_atropos_wf(
name="atropos_wf",
use_random_seed=True,
omp_nthreads=None,
mem_gb=3.0,
padding=10,
in_segmentation_model=tuple(ATROPOS_MODELS["T1w"].values()),
bspline_fitting_distance=200,
wm_prior=False,
):
"""
Create an ANTs' ATROPOS workflow for brain tissue segmentation.
Re-interprets supersteps 6 and 7 of ``antsBrainExtraction.sh``,
which refine the mask previously computed with the spatial
normalization to the template.
The workflow also executes steps 8 and 9 of the brain extraction
workflow.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from niworkflows.anat.ants import init_atropos_wf
wf = init_atropos_wf()
Parameters
----------
name : str, optional
Workflow name (default: "atropos_wf").
use_random_seed : bool
Whether ATROPOS should generate a random seed based on the
system's clock
omp_nthreads : int
Maximum number of threads an individual process may use
mem_gb : float
Estimated peak memory consumption of the most hungry nodes
in the workflow
padding : int
Pad images with zeros before processing
in_segmentation_model : tuple
A k-means segmentation is run to find gray or white matter
around the edge of the initial brain mask warped from the
template.
This produces a segmentation image with :math:`$K$` classes,
ordered by mean intensity in increasing order.
With this option, you can control :math:`$K$` and tell the script which
classes represent CSF, gray and white matter.
Format (K, csfLabel, gmLabel, wmLabel).
Examples:
``(3,1,2,3)`` for T1 with K=3, CSF=1, GM=2, WM=3 (default),
``(3,3,2,1)`` for T2 with K=3, CSF=3, GM=2, WM=1,
``(3,1,3,2)`` for FLAIR with K=3, CSF=1 GM=3, WM=2,
``(4,4,2,3)`` uses K=4, CSF=4, GM=2, WM=3.
bspline_fitting_distance : float
The size of the b-spline mesh grid elements, in mm (default: 200)
wm_prior : :obj:`bool`
Whether the WM posterior obtained with ATROPOS should be regularized with a prior
map (typically, mapped from the template). When ``wm_prior`` is ``True`` the input
field ``wm_prior`` of the input node must be connected.
Inputs
------
in_files : list
The original anatomical images passed in to the brain-extraction workflow.
in_corrected : list
:abbr:`INU (intensity non-uniformity)`-corrected files.
in_mask : str
Brain mask calculated previously.
wm_prior : :obj:`str`
Path to the WM prior probability map, aligned with the individual data.
Outputs
-------
out_file : :obj:`str`
Path of the corrected and brain-extracted result, using the ATROPOS refinement.
bias_corrected : :obj:`str`
Path of the corrected and result, using the ATROPOS refinement.
bias_image : :obj:`str`
Path of the estimated INU bias field, using the ATROPOS refinement.
out_mask : str
Refined brain mask
out_segm : str
Output segmentation
out_tpms : str
Output :abbr:`TPMs (tissue probability maps)`
"""
wf = pe.Workflow(name)
out_fields = [
"bias_corrected", "bias_image", "out_mask", "out_segm", "out_tpms"
]
inputnode = pe.Node(
niu.IdentityInterface(
fields=["in_files", "in_corrected", "in_mask", "wm_prior"]),
name="inputnode",
)
outputnode = pe.Node(niu.IdentityInterface(fields=["out_file"] +
out_fields),
name="outputnode")
copy_xform = pe.Node(CopyXForm(fields=out_fields),
name="copy_xform",
run_without_submitting=True)
# Morphological dilation, radius=2
dil_brainmask = pe.Node(ImageMath(operation="MD",
op2="2",
copy_header=True),
name="dil_brainmask")
# Get largest connected component
get_brainmask = pe.Node(
ImageMath(operation="GetLargestComponent", copy_header=True),
name="get_brainmask",
)
# Run atropos (core node)
atropos = pe.Node(
Atropos(
convergence_threshold=0.0,
dimension=3,
initialization="KMeans",
likelihood_model="Gaussian",
mrf_radius=[1, 1, 1],
mrf_smoothing_factor=0.1,
n_iterations=3,
number_of_tissue_classes=in_segmentation_model[0],
save_posteriors=True,
use_random_seed=use_random_seed,
),
name="01_atropos",
n_procs=omp_nthreads,
mem_gb=mem_gb,
)
# massage outputs
pad_segm = pe.Node(
ImageMath(operation="PadImage", op2=f"{padding}", copy_header=False),
name="02_pad_segm",
)
pad_mask = pe.Node(
ImageMath(operation="PadImage", op2=f"{padding}", copy_header=False),
name="03_pad_mask",
)
# Split segmentation in binary masks
sel_labels = pe.Node(
niu.Function(function=_select_labels,
output_names=["out_wm", "out_gm", "out_csf"]),
name="04_sel_labels",
)
sel_labels.inputs.labels = list(reversed(in_segmentation_model[1:]))
# Select largest components (GM, WM)
# ImageMath ${DIMENSION} ${EXTRACTION_WM} GetLargestComponent ${EXTRACTION_WM}
get_wm = pe.Node(ImageMath(operation="GetLargestComponent"),
name="05_get_wm")
get_gm = pe.Node(ImageMath(operation="GetLargestComponent"),
name="06_get_gm")
# Fill holes and calculate intersection
# ImageMath ${DIMENSION} ${EXTRACTION_TMP} FillHoles ${EXTRACTION_GM} 2
# MultiplyImages ${DIMENSION} ${EXTRACTION_GM} ${EXTRACTION_TMP} ${EXTRACTION_GM}
fill_gm = pe.Node(ImageMath(operation="FillHoles", op2="2"),
name="07_fill_gm")
mult_gm = pe.Node(
MultiplyImages(dimension=3, output_product_image="08_mult_gm.nii.gz"),
name="08_mult_gm",
)
# MultiplyImages ${DIMENSION} ${EXTRACTION_WM} ${ATROPOS_WM_CLASS_LABEL} ${EXTRACTION_WM}
# ImageMath ${DIMENSION} ${EXTRACTION_TMP} ME ${EXTRACTION_CSF} 10
relabel_wm = pe.Node(
MultiplyImages(
dimension=3,
second_input=in_segmentation_model[-1],
output_product_image="09_relabel_wm.nii.gz",
),
name="09_relabel_wm",
)
me_csf = pe.Node(ImageMath(operation="ME", op2="10"), name="10_me_csf")
# ImageMath ${DIMENSION} ${EXTRACTION_GM} addtozero ${EXTRACTION_GM} ${EXTRACTION_TMP}
# MultiplyImages ${DIMENSION} ${EXTRACTION_GM} ${ATROPOS_GM_CLASS_LABEL} ${EXTRACTION_GM}
# ImageMath ${DIMENSION} ${EXTRACTION_SEGMENTATION} addtozero ${EXTRACTION_WM} ${EXTRACTION_GM}
add_gm = pe.Node(ImageMath(operation="addtozero"), name="11_add_gm")
relabel_gm = pe.Node(
MultiplyImages(
dimension=3,
second_input=in_segmentation_model[-2],
output_product_image="12_relabel_gm.nii.gz",
),
name="12_relabel_gm",
)
add_gm_wm = pe.Node(ImageMath(operation="addtozero"), name="13_add_gm_wm")
# Superstep 7
# Split segmentation in binary masks
sel_labels2 = pe.Node(
niu.Function(function=_select_labels,
output_names=["out_gm", "out_wm"]),
name="14_sel_labels2",
)
sel_labels2.inputs.labels = in_segmentation_model[2:]
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} addtozero ${EXTRACTION_MASK} ${EXTRACTION_TMP}
add_7 = pe.Node(ImageMath(operation="addtozero"), name="15_add_7")
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} ME ${EXTRACTION_MASK} 2
me_7 = pe.Node(ImageMath(operation="ME", op2="2"), name="16_me_7")
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} GetLargestComponent ${EXTRACTION_MASK}
comp_7 = pe.Node(ImageMath(operation="GetLargestComponent"),
name="17_comp_7")
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} MD ${EXTRACTION_MASK} 4
md_7 = pe.Node(ImageMath(operation="MD", op2="4"), name="18_md_7")
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} FillHoles ${EXTRACTION_MASK} 2
fill_7 = pe.Node(ImageMath(operation="FillHoles", op2="2"),
name="19_fill_7")
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} addtozero ${EXTRACTION_MASK} \
# ${EXTRACTION_MASK_PRIOR_WARPED}
add_7_2 = pe.Node(ImageMath(operation="addtozero"), name="20_add_7_2")
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} MD ${EXTRACTION_MASK} 5
md_7_2 = pe.Node(ImageMath(operation="MD", op2="5"), name="21_md_7_2")
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} ME ${EXTRACTION_MASK} 5
me_7_2 = pe.Node(ImageMath(operation="ME", op2="5"), name="22_me_7_2")
# De-pad
depad_mask = pe.Node(ImageMath(operation="PadImage", op2="-%d" % padding),
name="23_depad_mask")
depad_segm = pe.Node(ImageMath(operation="PadImage", op2="-%d" % padding),
name="24_depad_segm")
depad_gm = pe.Node(ImageMath(operation="PadImage", op2="-%d" % padding),
name="25_depad_gm")
depad_wm = pe.Node(ImageMath(operation="PadImage", op2="-%d" % padding),
name="26_depad_wm")
depad_csf = pe.Node(ImageMath(operation="PadImage", op2="-%d" % padding),
name="27_depad_csf")
msk_conform = pe.Node(niu.Function(function=_conform_mask),
name="msk_conform")
merge_tpms = pe.Node(niu.Merge(in_segmentation_model[0]),
name="merge_tpms")
sel_wm = pe.Node(niu.Select(), name="sel_wm", run_without_submitting=True)
if not wm_prior:
sel_wm.inputs.index = in_segmentation_model[-1] - 1
copy_xform_wm = pe.Node(CopyXForm(fields=["wm_map"]),
name="copy_xform_wm",
run_without_submitting=True)
# Refine INU correction
inu_n4_final = pe.MapNode(
N4BiasFieldCorrection(
dimension=3,
save_bias=True,
copy_header=True,
n_iterations=[50] * 5,
convergence_threshold=1e-7,
shrink_factor=4,
bspline_fitting_distance=bspline_fitting_distance,
),
n_procs=omp_nthreads,
name="inu_n4_final",
iterfield=["input_image"],
)
try:
inu_n4_final.inputs.rescale_intensities = True
except ValueError:
warn(
"N4BiasFieldCorrection's --rescale-intensities option was added in ANTS 2.1.0 "
f"({inu_n4_final.interface.version} found.) Please consider upgrading.",
UserWarning,
)
# Apply mask
apply_mask = pe.MapNode(ApplyMask(),
iterfield=["in_file"],
name="apply_mask")
# fmt: off
wf.connect([
(inputnode, dil_brainmask, [("in_mask", "op1")]),
(inputnode, copy_xform, [(("in_files", _pop), "hdr_file")]),
(inputnode, copy_xform_wm, [(("in_files", _pop), "hdr_file")]),
(inputnode, pad_mask, [("in_mask", "op1")]),
(inputnode, atropos, [("in_corrected", "intensity_images")]),
(inputnode, inu_n4_final, [("in_files", "input_image")]),
(inputnode, msk_conform, [(("in_files", _pop), "in_reference")]),
(dil_brainmask, get_brainmask, [("output_image", "op1")]),
(get_brainmask, atropos, [("output_image", "mask_image")]),
(atropos, pad_segm, [("classified_image", "op1")]),
(pad_segm, sel_labels, [("output_image", "in_segm")]),
(sel_labels, get_wm, [("out_wm", "op1")]),
(sel_labels, get_gm, [("out_gm", "op1")]),
(get_gm, fill_gm, [("output_image", "op1")]),
(get_gm, mult_gm, [("output_image", "first_input")]),
(fill_gm, mult_gm, [("output_image", "second_input")]),
(get_wm, relabel_wm, [("output_image", "first_input")]),
(sel_labels, me_csf, [("out_csf", "op1")]),
(mult_gm, add_gm, [("output_product_image", "op1")]),
(me_csf, add_gm, [("output_image", "op2")]),
(add_gm, relabel_gm, [("output_image", "first_input")]),
(relabel_wm, add_gm_wm, [("output_product_image", "op1")]),
(relabel_gm, add_gm_wm, [("output_product_image", "op2")]),
(add_gm_wm, sel_labels2, [("output_image", "in_segm")]),
(sel_labels2, add_7, [("out_wm", "op1"), ("out_gm", "op2")]),
(add_7, me_7, [("output_image", "op1")]),
(me_7, comp_7, [("output_image", "op1")]),
(comp_7, md_7, [("output_image", "op1")]),
(md_7, fill_7, [("output_image", "op1")]),
(fill_7, add_7_2, [("output_image", "op1")]),
(pad_mask, add_7_2, [("output_image", "op2")]),
(add_7_2, md_7_2, [("output_image", "op1")]),
(md_7_2, me_7_2, [("output_image", "op1")]),
(me_7_2, depad_mask, [("output_image", "op1")]),
(add_gm_wm, depad_segm, [("output_image", "op1")]),
(relabel_wm, depad_wm, [("output_product_image", "op1")]),
(relabel_gm, depad_gm, [("output_product_image", "op1")]),
(sel_labels, depad_csf, [("out_csf", "op1")]),
(depad_csf, merge_tpms, [("output_image", "in1")]),
(depad_gm, merge_tpms, [("output_image", "in2")]),
(depad_wm, merge_tpms, [("output_image", "in3")]),
(depad_mask, msk_conform, [("output_image", "in_mask")]),
(msk_conform, copy_xform, [("out", "out_mask")]),
(depad_segm, copy_xform, [("output_image", "out_segm")]),
(merge_tpms, copy_xform, [("out", "out_tpms")]),
(atropos, sel_wm, [("posteriors", "inlist")]),
(sel_wm, copy_xform_wm, [("out", "wm_map")]),
(copy_xform_wm, inu_n4_final, [("wm_map", "weight_image")]),
(inu_n4_final, copy_xform, [("output_image", "bias_corrected"),
("bias_image", "bias_image")]),
(copy_xform, apply_mask, [("bias_corrected", "in_file"),
("out_mask", "in_mask")]),
(apply_mask, outputnode, [("out_file", "out_file")]),
(copy_xform, outputnode, [
("bias_corrected", "bias_corrected"),
("bias_image", "bias_image"),
("out_mask", "out_mask"),
("out_segm", "out_segm"),
("out_tpms", "out_tpms"),
]),
])
# fmt: on
if wm_prior:
from nipype.algorithms.metrics import FuzzyOverlap
def _argmax(in_dice):
import numpy as np
return np.argmax(in_dice)
match_wm = pe.Node(
niu.Function(function=_matchlen),
name="match_wm",
run_without_submitting=True,
)
overlap = pe.Node(FuzzyOverlap(),
name="overlap",
run_without_submitting=True)
apply_wm_prior = pe.Node(niu.Function(function=_improd),
name="apply_wm_prior")
# fmt: off
wf.disconnect([
(copy_xform_wm, inu_n4_final, [("wm_map", "weight_image")]),
])
wf.connect([
(inputnode, apply_wm_prior, [("in_mask", "in_mask"),
("wm_prior", "op2")]),
(inputnode, match_wm, [("wm_prior", "value")]),
(atropos, match_wm, [("posteriors", "reference")]),
(atropos, overlap, [("posteriors", "in_ref")]),
(match_wm, overlap, [("out", "in_tst")]),
(overlap, sel_wm, [(("class_fdi", _argmax), "index")]),
(copy_xform_wm, apply_wm_prior, [("wm_map", "op1")]),
(apply_wm_prior, inu_n4_final, [("out", "weight_image")]),
])
# fmt: on
return wf
def init_atropos_wf(name='atropos_wf',
use_random_seed=True,
omp_nthreads=None,
mem_gb=3.0,
padding=10,
in_segmentation_model=list(
ATROPOS_MODELS['T1w'].values())):
"""
Implements supersteps 6 and 7 of ``antsBrainExtraction.sh``,
which refine the mask previously computed with the spatial
normalization to the template.
**Parameters**
use_random_seed : bool
Whether ATROPOS should generate a random seed based on the
system's clock
omp_nthreads : int
Maximum number of threads an individual process may use
mem_gb : float
Estimated peak memory consumption of the most hungry nodes
in the workflow
padding : int
Pad images with zeros before processing
in_segmentation_model : tuple
A k-means segmentation is run to find gray or white matter
around the edge of the initial brain mask warped from the
template.
This produces a segmentation image with :math:`$K$` classes,
ordered by mean intensity in increasing order.
With this option, you can control :math:`$K$` and tell
the script which classes represent CSF, gray and white matter.
Format (K, csfLabel, gmLabel, wmLabel).
Examples:
- ``(3,1,2,3)`` for T1 with K=3, CSF=1, GM=2, WM=3 (default)
- ``(3,3,2,1)`` for T2 with K=3, CSF=3, GM=2, WM=1
- ``(3,1,3,2)`` for FLAIR with K=3, CSF=1 GM=3, WM=2
- ``(4,4,2,3)`` uses K=4, CSF=4, GM=2, WM=3
name : str, optional
Workflow name (default: atropos_wf)
**Inputs**
in_files
:abbr:`INU (intensity non-uniformity)`-corrected files.
in_mask
Brain mask calculated previously
**Outputs**
out_mask
Refined brain mask
out_segm
Output segmentation
out_tpms
Output :abbr:`TPMs (tissue probability maps)`
"""
wf = pe.Workflow(name)
inputnode = pe.Node(niu.IdentityInterface(
fields=['in_files', 'in_mask', 'in_mask_dilated']),
name='inputnode')
outputnode = pe.Node(
niu.IdentityInterface(fields=['out_mask', 'out_segm', 'out_tpms']),
name='outputnode')
copy_xform = pe.Node(
CopyXForm(fields=['out_mask', 'out_segm', 'out_tpms']),
name='copy_xform',
run_without_submitting=True,
mem_gb=2.5)
# Run atropos (core node)
atropos = pe.Node(Atropos(
dimension=3,
initialization='KMeans',
number_of_tissue_classes=in_segmentation_model[0],
n_iterations=3,
convergence_threshold=0.0,
mrf_radius=[1, 1, 1],
mrf_smoothing_factor=0.1,
likelihood_model='Gaussian',
use_random_seed=use_random_seed),
name='01_atropos',
n_procs=omp_nthreads,
mem_gb=mem_gb)
# massage outputs
pad_segm = pe.Node(ImageMath(operation='PadImage', op2='%d' % padding),
name='02_pad_segm')
pad_mask = pe.Node(ImageMath(operation='PadImage', op2='%d' % padding),
name='03_pad_mask')
# Split segmentation in binary masks
sel_labels = pe.Node(niu.Function(
function=_select_labels, output_names=['out_wm', 'out_gm', 'out_csf']),
name='04_sel_labels')
sel_labels.inputs.labels = list(reversed(in_segmentation_model[1:]))
# Select largest components (GM, WM)
# ImageMath ${DIMENSION} ${EXTRACTION_WM} GetLargestComponent ${EXTRACTION_WM}
get_wm = pe.Node(ImageMath(operation='GetLargestComponent'),
name='05_get_wm')
get_gm = pe.Node(ImageMath(operation='GetLargestComponent'),
name='06_get_gm')
# Fill holes and calculate intersection
# ImageMath ${DIMENSION} ${EXTRACTION_TMP} FillHoles ${EXTRACTION_GM} 2
# MultiplyImages ${DIMENSION} ${EXTRACTION_GM} ${EXTRACTION_TMP} ${EXTRACTION_GM}
fill_gm = pe.Node(ImageMath(operation='FillHoles', op2='2'),
name='07_fill_gm')
mult_gm = pe.Node(MultiplyImages(dimension=3,
output_product_image='08_mult_gm.nii.gz'),
name='08_mult_gm')
# MultiplyImages ${DIMENSION} ${EXTRACTION_WM} ${ATROPOS_WM_CLASS_LABEL} ${EXTRACTION_WM}
# ImageMath ${DIMENSION} ${EXTRACTION_TMP} ME ${EXTRACTION_CSF} 10
relabel_wm = pe.Node(MultiplyImages(
dimension=3,
second_input=in_segmentation_model[-1],
output_product_image='09_relabel_wm.nii.gz'),
name='09_relabel_wm')
me_csf = pe.Node(ImageMath(operation='ME', op2='10'), name='10_me_csf')
# ImageMath ${DIMENSION} ${EXTRACTION_GM} addtozero ${EXTRACTION_GM} ${EXTRACTION_TMP}
# MultiplyImages ${DIMENSION} ${EXTRACTION_GM} ${ATROPOS_GM_CLASS_LABEL} ${EXTRACTION_GM}
# ImageMath ${DIMENSION} ${EXTRACTION_SEGMENTATION} addtozero ${EXTRACTION_WM} ${EXTRACTION_GM}
add_gm = pe.Node(ImageMath(operation='addtozero'), name='11_add_gm')
relabel_gm = pe.Node(MultiplyImages(
dimension=3,
second_input=in_segmentation_model[-2],
output_product_image='12_relabel_gm.nii.gz'),
name='12_relabel_gm')
add_gm_wm = pe.Node(ImageMath(operation='addtozero'), name='13_add_gm_wm')
# Superstep 7
# Split segmentation in binary masks
sel_labels2 = pe.Node(niu.Function(function=_select_labels,
output_names=['out_gm', 'out_wm']),
name='14_sel_labels2')
sel_labels2.inputs.labels = in_segmentation_model[2:]
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} addtozero ${EXTRACTION_MASK} ${EXTRACTION_TMP}
add_7 = pe.Node(ImageMath(operation='addtozero'), name='15_add_7')
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} ME ${EXTRACTION_MASK} 2
me_7 = pe.Node(ImageMath(operation='ME', op2='2'), name='16_me_7')
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} GetLargestComponent ${EXTRACTION_MASK}
comp_7 = pe.Node(ImageMath(operation='GetLargestComponent'),
name='17_comp_7')
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} MD ${EXTRACTION_MASK} 4
md_7 = pe.Node(ImageMath(operation='MD', op2='4'), name='18_md_7')
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} FillHoles ${EXTRACTION_MASK} 2
fill_7 = pe.Node(ImageMath(operation='FillHoles', op2='2'),
name='19_fill_7')
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} addtozero ${EXTRACTION_MASK} \
# ${EXTRACTION_MASK_PRIOR_WARPED}
add_7_2 = pe.Node(ImageMath(operation='addtozero'), name='20_add_7_2')
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} MD ${EXTRACTION_MASK} 5
md_7_2 = pe.Node(ImageMath(operation='MD', op2='5'), name='21_md_7_2')
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} ME ${EXTRACTION_MASK} 5
me_7_2 = pe.Node(ImageMath(operation='ME', op2='5'), name='22_me_7_2')
# De-pad
depad_mask = pe.Node(ImageMath(operation='PadImage', op2='-%d' % padding),
name='23_depad_mask')
depad_segm = pe.Node(ImageMath(operation='PadImage', op2='-%d' % padding),
name='24_depad_segm')
depad_gm = pe.Node(ImageMath(operation='PadImage', op2='-%d' % padding),
name='25_depad_gm')
depad_wm = pe.Node(ImageMath(operation='PadImage', op2='-%d' % padding),
name='26_depad_wm')
depad_csf = pe.Node(ImageMath(operation='PadImage', op2='-%d' % padding),
name='27_depad_csf')
msk_conform = pe.Node(niu.Function(function=_conform_mask),
name='msk_conform')
merge_tpms = pe.Node(niu.Merge(in_segmentation_model[0]),
name='merge_tpms')
wf.connect([
(inputnode, copy_xform, [(('in_files', _pop), 'hdr_file')]),
(inputnode, pad_mask, [('in_mask', 'op1')]),
(inputnode, atropos, [('in_files', 'intensity_images'),
('in_mask_dilated', 'mask_image')]),
(inputnode, msk_conform, [(('in_files', _pop), 'in_reference')]),
(atropos, pad_segm, [('classified_image', 'op1')]),
(pad_segm, sel_labels, [('output_image', 'in_segm')]),
(sel_labels, get_wm, [('out_wm', 'op1')]),
(sel_labels, get_gm, [('out_gm', 'op1')]),
(get_gm, fill_gm, [('output_image', 'op1')]),
(get_gm, mult_gm, [('output_image', 'first_input')]),
(fill_gm, mult_gm, [('output_image', 'second_input')]),
(get_wm, relabel_wm, [('output_image', 'first_input')]),
(sel_labels, me_csf, [('out_csf', 'op1')]),
(mult_gm, add_gm, [('output_product_image', 'op1')]),
(me_csf, add_gm, [('output_image', 'op2')]),
(add_gm, relabel_gm, [('output_image', 'first_input')]),
(relabel_wm, add_gm_wm, [('output_product_image', 'op1')]),
(relabel_gm, add_gm_wm, [('output_product_image', 'op2')]),
(add_gm_wm, sel_labels2, [('output_image', 'in_segm')]),
(sel_labels2, add_7, [('out_wm', 'op1'), ('out_gm', 'op2')]),
(add_7, me_7, [('output_image', 'op1')]),
(me_7, comp_7, [('output_image', 'op1')]),
(comp_7, md_7, [('output_image', 'op1')]),
(md_7, fill_7, [('output_image', 'op1')]),
(fill_7, add_7_2, [('output_image', 'op1')]),
(pad_mask, add_7_2, [('output_image', 'op2')]),
(add_7_2, md_7_2, [('output_image', 'op1')]),
(md_7_2, me_7_2, [('output_image', 'op1')]),
(me_7_2, depad_mask, [('output_image', 'op1')]),
(add_gm_wm, depad_segm, [('output_image', 'op1')]),
(relabel_wm, depad_wm, [('output_product_image', 'op1')]),
(relabel_gm, depad_gm, [('output_product_image', 'op1')]),
(sel_labels, depad_csf, [('out_csf', 'op1')]),
(depad_csf, merge_tpms, [('output_image', 'in1')]),
(depad_gm, merge_tpms, [('output_image', 'in2')]),
(depad_wm, merge_tpms, [('output_image', 'in3')]),
(depad_mask, msk_conform, [('output_image', 'in_mask')]),
(msk_conform, copy_xform, [('out', 'out_mask')]),
(depad_segm, copy_xform, [('output_image', 'out_segm')]),
(merge_tpms, copy_xform, [('out', 'out_tpms')]),
(copy_xform, outputnode, [('out_mask', 'out_mask'),
('out_segm', 'out_segm'),
('out_tpms', 'out_tpms')]),
])
return wf
def init_atropos_wf(
name="atropos_wf",
use_random_seed=True,
omp_nthreads=None,
mem_gb=3.0,
padding=10,
in_segmentation_model=tuple(ATROPOS_MODELS["T1w"].values()),
):
"""
Create an ANTs' ATROPOS workflow for brain tissue segmentation.
Implements supersteps 6 and 7 of ``antsBrainExtraction.sh``,
which refine the mask previously computed with the spatial
normalization to the template.
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from niworkflows.anat.ants import init_atropos_wf
wf = init_atropos_wf()
Parameters
----------
use_random_seed : bool
Whether ATROPOS should generate a random seed based on the
system's clock
omp_nthreads : int
Maximum number of threads an individual process may use
mem_gb : float
Estimated peak memory consumption of the most hungry nodes
in the workflow
padding : int
Pad images with zeros before processing
in_segmentation_model : tuple
A k-means segmentation is run to find gray or white matter
around the edge of the initial brain mask warped from the
template.
This produces a segmentation image with :math:`$K$` classes,
ordered by mean intensity in increasing order.
With this option, you can control :math:`$K$` and tell the script which
classes represent CSF, gray and white matter.
Format (K, csfLabel, gmLabel, wmLabel).
Examples:
``(3,1,2,3)`` for T1 with K=3, CSF=1, GM=2, WM=3 (default),
``(3,3,2,1)`` for T2 with K=3, CSF=3, GM=2, WM=1,
``(3,1,3,2)`` for FLAIR with K=3, CSF=1 GM=3, WM=2,
``(4,4,2,3)`` uses K=4, CSF=4, GM=2, WM=3.
name : str, optional
Workflow name (default: "atropos_wf").
Inputs
------
in_files : list
:abbr:`INU (intensity non-uniformity)`-corrected files.
in_mask : str
Brain mask calculated previously.
Outputs
-------
out_mask : str
Refined brain mask
out_segm : str
Output segmentation
out_tpms : str
Output :abbr:`TPMs (tissue probability maps)`
"""
wf = pe.Workflow(name)
inputnode = pe.Node(
niu.IdentityInterface(
fields=["in_files", "in_mask", "in_mask_dilated"]),
name="inputnode",
)
outputnode = pe.Node(
niu.IdentityInterface(fields=["out_mask", "out_segm", "out_tpms"]),
name="outputnode",
)
copy_xform = pe.Node(
CopyXForm(fields=["out_mask", "out_segm", "out_tpms"]),
name="copy_xform",
run_without_submitting=True,
)
# Run atropos (core node)
atropos = pe.Node(
Atropos(
dimension=3,
initialization="KMeans",
number_of_tissue_classes=in_segmentation_model[0],
n_iterations=3,
convergence_threshold=0.0,
mrf_radius=[1, 1, 1],
mrf_smoothing_factor=0.1,
likelihood_model="Gaussian",
use_random_seed=use_random_seed,
),
name="01_atropos",
n_procs=omp_nthreads,
mem_gb=mem_gb,
)
# massage outputs
pad_segm = pe.Node(ImageMath(operation="PadImage", op2="%d" % padding),
name="02_pad_segm")
pad_mask = pe.Node(ImageMath(operation="PadImage", op2="%d" % padding),
name="03_pad_mask")
# Split segmentation in binary masks
sel_labels = pe.Node(
niu.Function(function=_select_labels,
output_names=["out_wm", "out_gm", "out_csf"]),
name="04_sel_labels",
)
sel_labels.inputs.labels = list(reversed(in_segmentation_model[1:]))
# Select largest components (GM, WM)
# ImageMath ${DIMENSION} ${EXTRACTION_WM} GetLargestComponent ${EXTRACTION_WM}
get_wm = pe.Node(ImageMath(operation="GetLargestComponent"),
name="05_get_wm")
get_gm = pe.Node(ImageMath(operation="GetLargestComponent"),
name="06_get_gm")
# Fill holes and calculate intersection
# ImageMath ${DIMENSION} ${EXTRACTION_TMP} FillHoles ${EXTRACTION_GM} 2
# MultiplyImages ${DIMENSION} ${EXTRACTION_GM} ${EXTRACTION_TMP} ${EXTRACTION_GM}
fill_gm = pe.Node(ImageMath(operation="FillHoles", op2="2"),
name="07_fill_gm")
mult_gm = pe.Node(
MultiplyImages(dimension=3, output_product_image="08_mult_gm.nii.gz"),
name="08_mult_gm",
)
# MultiplyImages ${DIMENSION} ${EXTRACTION_WM} ${ATROPOS_WM_CLASS_LABEL} ${EXTRACTION_WM}
# ImageMath ${DIMENSION} ${EXTRACTION_TMP} ME ${EXTRACTION_CSF} 10
relabel_wm = pe.Node(
MultiplyImages(
dimension=3,
second_input=in_segmentation_model[-1],
output_product_image="09_relabel_wm.nii.gz",
),
name="09_relabel_wm",
)
me_csf = pe.Node(ImageMath(operation="ME", op2="10"), name="10_me_csf")
# ImageMath ${DIMENSION} ${EXTRACTION_GM} addtozero ${EXTRACTION_GM} ${EXTRACTION_TMP}
# MultiplyImages ${DIMENSION} ${EXTRACTION_GM} ${ATROPOS_GM_CLASS_LABEL} ${EXTRACTION_GM}
# ImageMath ${DIMENSION} ${EXTRACTION_SEGMENTATION} addtozero ${EXTRACTION_WM} ${EXTRACTION_GM}
add_gm = pe.Node(ImageMath(operation="addtozero"), name="11_add_gm")
relabel_gm = pe.Node(
MultiplyImages(
dimension=3,
second_input=in_segmentation_model[-2],
output_product_image="12_relabel_gm.nii.gz",
),
name="12_relabel_gm",
)
add_gm_wm = pe.Node(ImageMath(operation="addtozero"), name="13_add_gm_wm")
# Superstep 7
# Split segmentation in binary masks
sel_labels2 = pe.Node(
niu.Function(function=_select_labels,
output_names=["out_gm", "out_wm"]),
name="14_sel_labels2",
)
sel_labels2.inputs.labels = in_segmentation_model[2:]
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} addtozero ${EXTRACTION_MASK} ${EXTRACTION_TMP}
add_7 = pe.Node(ImageMath(operation="addtozero"), name="15_add_7")
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} ME ${EXTRACTION_MASK} 2
me_7 = pe.Node(ImageMath(operation="ME", op2="2"), name="16_me_7")
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} GetLargestComponent ${EXTRACTION_MASK}
comp_7 = pe.Node(ImageMath(operation="GetLargestComponent"),
name="17_comp_7")
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} MD ${EXTRACTION_MASK} 4
md_7 = pe.Node(ImageMath(operation="MD", op2="4"), name="18_md_7")
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} FillHoles ${EXTRACTION_MASK} 2
fill_7 = pe.Node(ImageMath(operation="FillHoles", op2="2"),
name="19_fill_7")
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} addtozero ${EXTRACTION_MASK} \
# ${EXTRACTION_MASK_PRIOR_WARPED}
add_7_2 = pe.Node(ImageMath(operation="addtozero"), name="20_add_7_2")
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} MD ${EXTRACTION_MASK} 5
md_7_2 = pe.Node(ImageMath(operation="MD", op2="5"), name="21_md_7_2")
# ImageMath ${DIMENSION} ${EXTRACTION_MASK} ME ${EXTRACTION_MASK} 5
me_7_2 = pe.Node(ImageMath(operation="ME", op2="5"), name="22_me_7_2")
# De-pad
depad_mask = pe.Node(ImageMath(operation="PadImage", op2="-%d" % padding),
name="23_depad_mask")
depad_segm = pe.Node(ImageMath(operation="PadImage", op2="-%d" % padding),
name="24_depad_segm")
depad_gm = pe.Node(ImageMath(operation="PadImage", op2="-%d" % padding),
name="25_depad_gm")
depad_wm = pe.Node(ImageMath(operation="PadImage", op2="-%d" % padding),
name="26_depad_wm")
depad_csf = pe.Node(ImageMath(operation="PadImage", op2="-%d" % padding),
name="27_depad_csf")
msk_conform = pe.Node(niu.Function(function=_conform_mask),
name="msk_conform")
merge_tpms = pe.Node(niu.Merge(in_segmentation_model[0]),
name="merge_tpms")
# fmt: off
wf.connect([
(inputnode, copy_xform, [(("in_files", _pop), "hdr_file")]),
(inputnode, pad_mask, [("in_mask", "op1")]),
(inputnode, atropos, [
("in_files", "intensity_images"),
("in_mask_dilated", "mask_image"),
]),
(inputnode, msk_conform, [(("in_files", _pop), "in_reference")]),
(atropos, pad_segm, [("classified_image", "op1")]),
(pad_segm, sel_labels, [("output_image", "in_segm")]),
(sel_labels, get_wm, [("out_wm", "op1")]),
(sel_labels, get_gm, [("out_gm", "op1")]),
(get_gm, fill_gm, [("output_image", "op1")]),
(get_gm, mult_gm, [("output_image", "first_input")]),
(fill_gm, mult_gm, [("output_image", "second_input")]),
(get_wm, relabel_wm, [("output_image", "first_input")]),
(sel_labels, me_csf, [("out_csf", "op1")]),
(mult_gm, add_gm, [("output_product_image", "op1")]),
(me_csf, add_gm, [("output_image", "op2")]),
(add_gm, relabel_gm, [("output_image", "first_input")]),
(relabel_wm, add_gm_wm, [("output_product_image", "op1")]),
(relabel_gm, add_gm_wm, [("output_product_image", "op2")]),
(add_gm_wm, sel_labels2, [("output_image", "in_segm")]),
(sel_labels2, add_7, [("out_wm", "op1"), ("out_gm", "op2")]),
(add_7, me_7, [("output_image", "op1")]),
(me_7, comp_7, [("output_image", "op1")]),
(comp_7, md_7, [("output_image", "op1")]),
(md_7, fill_7, [("output_image", "op1")]),
(fill_7, add_7_2, [("output_image", "op1")]),
(pad_mask, add_7_2, [("output_image", "op2")]),
(add_7_2, md_7_2, [("output_image", "op1")]),
(md_7_2, me_7_2, [("output_image", "op1")]),
(me_7_2, depad_mask, [("output_image", "op1")]),
(add_gm_wm, depad_segm, [("output_image", "op1")]),
(relabel_wm, depad_wm, [("output_product_image", "op1")]),
(relabel_gm, depad_gm, [("output_product_image", "op1")]),
(sel_labels, depad_csf, [("out_csf", "op1")]),
(depad_csf, merge_tpms, [("output_image", "in1")]),
(depad_gm, merge_tpms, [("output_image", "in2")]),
(depad_wm, merge_tpms, [("output_image", "in3")]),
(depad_mask, msk_conform, [("output_image", "in_mask")]),
(msk_conform, copy_xform, [("out", "out_mask")]),
(depad_segm, copy_xform, [("output_image", "out_segm")]),
(merge_tpms, copy_xform, [("out", "out_tpms")]),
(copy_xform, outputnode, [
("out_mask", "out_mask"),
("out_segm", "out_segm"),
("out_tpms", "out_tpms"),
]),
])
# fmt: on
return wf