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ants.py
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# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""Nipype translation of ANTs' workflows."""
# general purpose
from collections import OrderedDict
from multiprocessing import cpu_count
from pkg_resources import resource_filename as pkgr_fn
from packaging.version import parse as parseversion, Version
from warnings import warn
# nipype
from nipype.pipeline import engine as pe
from nipype.interfaces import utility as niu
from nipype.interfaces.fsl.maths import ApplyMask
from nipype.interfaces.ants import N4BiasFieldCorrection, Atropos, MultiplyImages
from ..utils.misc import get_template_specs
from ..utils.connections import pop_file as _pop
# niworkflows
from ..interfaces.ants import (
ImageMath,
ResampleImageBySpacing,
AI,
ThresholdImage,
)
from ..interfaces.fixes import (
FixHeaderRegistration as Registration,
FixHeaderApplyTransforms as ApplyTransforms,
)
from ..interfaces.utils import CopyXForm
from ..interfaces.nibabel import Binarize
ATROPOS_MODELS = {
"T1w": OrderedDict([("nclasses", 3), ("csf", 1), ("gm", 2), ("wm", 3)]),
"T2w": OrderedDict([("nclasses", 3), ("csf", 3), ("gm", 2), ("wm", 1)]),
"FLAIR": OrderedDict([("nclasses", 3), ("csf", 1), ("gm", 3), ("wm", 2)]),
}
def init_brain_extraction_wf(
name="brain_extraction_wf",
in_template="OASIS30ANTs",
template_spec=None,
use_float=True,
normalization_quality="precise",
omp_nthreads=None,
mem_gb=3.0,
bids_suffix="T1w",
atropos_refine=True,
atropos_use_random_seed=True,
atropos_model=None,
use_laplacian=True,
bspline_fitting_distance=200,
):
"""
Build a workflow for atlas-based brain extraction on anatomical MRI data.
A Nipype implementation of the official ANTs' ``antsBrainExtraction.sh``
workflow (only for 3D images).
The official workflow is built as follows (and this implementation
follows the same organization):
1. Step 1 performs several clerical tasks (adding padding, calculating
the Laplacian of inputs, affine initialization) and the core
spatial normalization.
2. Maps the brain mask into target space using the normalization
calculated in 1.
3. Superstep 1b: smart binarization of the brain mask
4. Superstep 6: apply ATROPOS and massage its outputs
5. Superstep 7: use results from 4 to refine the brain mask
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from aslprep.niworkflows.anat.ants import init_brain_extraction_wf
wf = init_brain_extraction_wf()
Parameters
----------
in_template : str
Name of the skull-stripping template ('OASIS30ANTs', 'NKI', or
path).
The brain template from which regions will be projected
Anatomical template created using e.g. LPBA40 data set with
``buildtemplateparallel.sh`` in ANTs.
The workflow will automatically search for a 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.
use_float : bool
Whether single precision should be used
normalization_quality : str
Use more precise or faster registration parameters
(default: ``precise``, other possible values: ``testing``)
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
bids_suffix : str
Sequence type of the first input image. For a list of acceptable values
see https://bids-specification.readthedocs.io/en/latest/\
04-modality-specific-files/01-magnetic-resonance-imaging-data.html#anatomy-imaging-data
atropos_refine : bool
Enables or disables the whole ATROPOS sub-workflow
atropos_use_random_seed : bool
Whether ATROPOS should generate a random seed based on the
system's clock
atropos_model : tuple or None
Allows to specify a particular segmentation model, overwriting
the defaults based on ``bids_suffix``
use_laplacian : bool
Enables or disables alignment of the Laplacian as an additional
criterion for image registration quality (default: True)
bspline_fitting_distance : float
The size of the b-spline mesh grid elements, in mm (default: 200)
name : str, optional
Workflow name (default: antsBrainExtraction)
Inputs
------
in_files : list
List of input anatomical images to be brain-extracted,
typically T1-weighted.
If a list of anatomical images is provided, 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 T1w as the first image.
in_mask : list, optional
Mask used for registration to limit the metric
computation to a specific region.
Outputs
-------
out_file : str
Skull-stripped and :abbr:`INU (intensity non-uniformity)`-corrected ``in_files``
out_mask : str
Calculated brain mask
bias_corrected : str
The ``in_files`` input images, after :abbr:`INU (intensity non-uniformity)`
correction, before skull-stripping.
bias_image : str
The :abbr:`INU (intensity non-uniformity)` field estimated for each
input in ``in_files``
out_segm : str
Output segmentation by ATROPOS
out_tpms : str
Output :abbr:`TPMs (tissue probability maps)` by ATROPOS
"""
from templateflow.api import get as get_template
wf = pe.Workflow(name)
template_spec = template_spec or {}
# suffix passed via spec takes precedence
template_spec["suffix"] = template_spec.get("suffix", bids_suffix)
tpl_target_path, common_spec = get_template_specs(
in_template, template_spec=template_spec
)
# Get probabilistic brain mask if available
tpl_mask_path = get_template(
in_template, label="brain", suffix="probseg", **common_spec
) or get_template(in_template, desc="brain", suffix="mask", **common_spec)
if omp_nthreads is None or omp_nthreads < 1:
omp_nthreads = cpu_count()
inputnode = pe.Node(
niu.IdentityInterface(fields=["in_files", "in_mask"]), name="inputnode"
)
# Try to find a registration mask, set if available
tpl_regmask_path = get_template(
in_template, desc="BrainCerebellumExtraction", suffix="mask", **common_spec
)
if tpl_regmask_path:
inputnode.inputs.in_mask = str(tpl_regmask_path)
outputnode = pe.Node(
niu.IdentityInterface(
fields=[
"out_file",
"out_mask",
"bias_corrected",
"bias_image",
"out_segm",
"out_tpms",
]
),
name="outputnode",
)
copy_xform = pe.Node(
CopyXForm(fields=["out_file", "out_mask", "bias_corrected", "bias_image"]),
name="copy_xform",
run_without_submitting=True,
)
trunc = pe.MapNode(
ImageMath(operation="TruncateImageIntensity", op2="0.01 0.999 256"),
name="truncate_images",
iterfield=["op1"],
)
inu_n4 = pe.MapNode(
N4BiasFieldCorrection(
dimension=3,
save_bias=False,
copy_header=True,
n_iterations=[50] * 4,
convergence_threshold=1e-7,
shrink_factor=4,
bspline_fitting_distance=bspline_fitting_distance,
),
n_procs=omp_nthreads,
name="inu_n4",
iterfield=["input_image"],
)
res_tmpl = pe.Node(
ResampleImageBySpacing(out_spacing=(4, 4, 4), apply_smoothing=True),
name="res_tmpl",
)
res_tmpl.inputs.input_image = tpl_target_path
res_target = pe.Node(
ResampleImageBySpacing(out_spacing=(4, 4, 4), apply_smoothing=True),
name="res_target",
)
lap_tmpl = pe.Node(ImageMath(operation="Laplacian", op2="1.5 1"), name="lap_tmpl")
lap_tmpl.inputs.op1 = tpl_target_path
lap_target = pe.Node(
ImageMath(operation="Laplacian", op2="1.5 1"), name="lap_target"
)
mrg_tmpl = pe.Node(niu.Merge(2), name="mrg_tmpl")
mrg_tmpl.inputs.in1 = tpl_target_path
mrg_target = pe.Node(niu.Merge(2), name="mrg_target")
# Initialize transforms with antsAI
init_aff = pe.Node(
AI(
metric=("Mattes", 32, "Regular", 0.25),
transform=("Affine", 0.1),
search_factor=(15, 0.1),
principal_axes=False,
convergence=(10, 1e-6, 10),
verbose=True,
),
name="init_aff",
n_procs=omp_nthreads,
)
# Tolerate missing ANTs at construction time
_ants_version = Registration().version
if _ants_version and parseversion(_ants_version) >= Version("2.3.0"):
init_aff.inputs.search_grid = (40, (0, 40, 40))
# Set up spatial normalization
settings_file = (
"antsBrainExtraction_%s.json"
if use_laplacian
else "antsBrainExtractionNoLaplacian_%s.json"
)
norm = pe.Node(
Registration(
from_file=pkgr_fn("niworkflows.data", settings_file % normalization_quality)
),
name="norm",
n_procs=omp_nthreads,
mem_gb=mem_gb,
)
norm.inputs.float = use_float
fixed_mask_trait = "fixed_image_mask"
if _ants_version and parseversion(_ants_version) >= Version("2.2.0"):
fixed_mask_trait += "s"
map_brainmask = pe.Node(
ApplyTransforms(interpolation="Gaussian", float=True),
name="map_brainmask",
mem_gb=1,
)
map_brainmask.inputs.input_image = str(tpl_mask_path)
thr_brainmask = pe.Node(
ThresholdImage(
dimension=3, th_low=0.5, th_high=1.0, inside_value=1, outside_value=0
),
name="thr_brainmask",
)
# Morphological dilation, radius=2
dil_brainmask = pe.Node(ImageMath(operation="MD", op2="2"), name="dil_brainmask")
# Get largest connected component
get_brainmask = pe.Node(
ImageMath(operation="GetLargestComponent"), name="get_brainmask"
)
# 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"],
)
if _ants_version and parseversion(_ants_version) >= Version("2.1.0"):
inu_n4_final.inputs.rescale_intensities = True
else:
warn(
"""\
Found ANTs version %s, which is too old. Please consider upgrading to 2.1.0 or \
greater so that the --rescale-intensities option is available with \
N4BiasFieldCorrection."""
% _ants_version,
DeprecationWarning,
)
# Apply mask
apply_mask = pe.MapNode(ApplyMask(), iterfield=["in_file"], name="apply_mask")
# fmt: off
wf.connect([
(inputnode, trunc, [("in_files", "op1")]),
(inputnode, copy_xform, [(("in_files", _pop), "hdr_file")]),
(inputnode, inu_n4_final, [("in_files", "input_image")]),
(inputnode, init_aff, [("in_mask", "fixed_image_mask")]),
(inputnode, norm, [("in_mask", fixed_mask_trait)]),
(inputnode, map_brainmask, [(("in_files", _pop), "reference_image")]),
(trunc, inu_n4, [("output_image", "input_image")]),
(inu_n4, res_target, [(("output_image", _pop), "input_image")]),
(res_tmpl, init_aff, [("output_image", "fixed_image")]),
(res_target, init_aff, [("output_image", "moving_image")]),
(init_aff, norm, [("output_transform", "initial_moving_transform")]),
(norm, map_brainmask, [
("reverse_transforms", "transforms"),
("reverse_invert_flags", "invert_transform_flags"),
]),
(map_brainmask, thr_brainmask, [("output_image", "input_image")]),
(thr_brainmask, dil_brainmask, [("output_image", "op1")]),
(dil_brainmask, get_brainmask, [("output_image", "op1")]),
(inu_n4_final, apply_mask, [("output_image", "in_file")]),
(get_brainmask, apply_mask, [("output_image", "mask_file")]),
(get_brainmask, copy_xform, [("output_image", "out_mask")]),
(apply_mask, copy_xform, [("out_file", "out_file")]),
(inu_n4_final, copy_xform, [
("output_image", "bias_corrected"),
("bias_image", "bias_image"),
]),
(copy_xform, outputnode, [
("out_file", "out_file"),
("out_mask", "out_mask"),
("bias_corrected", "bias_corrected"),
("bias_image", "bias_image"),
]),
])
# fmt: on
if use_laplacian:
lap_tmpl = pe.Node(
ImageMath(operation="Laplacian", op2="1.5 1"), name="lap_tmpl"
)
lap_tmpl.inputs.op1 = tpl_target_path
lap_target = pe.Node(
ImageMath(operation="Laplacian", op2="1.5 1"), name="lap_target"
)
mrg_tmpl = pe.Node(niu.Merge(2), name="mrg_tmpl")
mrg_tmpl.inputs.in1 = tpl_target_path
mrg_target = pe.Node(niu.Merge(2), name="mrg_target")
# fmt: off
wf.connect([
(inu_n4, lap_target, [(("output_image", _pop), "op1")]),
(lap_tmpl, mrg_tmpl, [("output_image", "in2")]),
(inu_n4, mrg_target, [("output_image", "in1")]),
(lap_target, mrg_target, [("output_image", "in2")]),
(mrg_tmpl, norm, [("out", "fixed_image")]),
(mrg_target, norm, [("out", "moving_image")]),
])
# fmt: on
else:
norm.inputs.fixed_image = tpl_target_path
# fmt: off
wf.connect([
(inu_n4, norm, [(("output_image", _pop), "moving_image")]),
])
# fmt: on
if atropos_refine:
atropos_model = atropos_model or list(ATROPOS_MODELS[bids_suffix].values())
atropos_wf = init_atropos_wf(
use_random_seed=atropos_use_random_seed,
omp_nthreads=omp_nthreads,
mem_gb=mem_gb,
in_segmentation_model=atropos_model,
)
sel_wm = pe.Node(
niu.Select(index=atropos_model[-1] - 1),
name="sel_wm",
run_without_submitting=True,
)
# fmt: off
wf.disconnect([
(get_brainmask, apply_mask, [("output_image", "mask_file")]),
(copy_xform, outputnode, [("out_mask", "out_mask")]),
])
wf.connect([
(inu_n4, atropos_wf, [("output_image", "inputnode.in_files")]),
(thr_brainmask, atropos_wf, [("output_image", "inputnode.in_mask")]),
(get_brainmask, atropos_wf, [
("output_image", "inputnode.in_mask_dilated"),
]),
(atropos_wf, sel_wm, [("outputnode.out_tpms", "inlist")]),
(sel_wm, inu_n4_final, [("out", "weight_image")]),
(atropos_wf, apply_mask, [("outputnode.out_mask", "mask_file")]),
(atropos_wf, outputnode, [
("outputnode.out_mask", "out_mask"),
("outputnode.out_segm", "out_segm"),
("outputnode.out_tpms", "out_tpms"),
]),
])
# 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=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 aslprep.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
def init_n4_only_wf(
atropos_model=None,
atropos_refine=True,
atropos_use_random_seed=True,
bids_suffix="T1w",
mem_gb=3.0,
name="n4_only_wf",
omp_nthreads=None,
):
"""
Build a workflow to sidetrack brain extraction on skull-stripped datasets.
An alternative workflow to "init_brain_extraction_wf", for anatomical
images which have already been brain extracted.
1. Creates brain mask assuming all zero voxels are outside the brain
2. Applies N4 bias field correction
3. (Optional) apply ATROPOS and massage its outputs
4. Use results from 3 to refine N4 bias field correction
Workflow Graph
.. workflow::
:graph2use: orig
:simple_form: yes
from aslprep.niworkflows.anat.ants import init_n4_only_wf
wf = init_n4_only_wf()
Parameters
----------
omp_nthreads : int
Maximum number of threads an individual process may use
mem_gb : float
Estimated peak memory consumption of the most hungry nodes
bids_suffix : str
Sequence type of the first input image. For a list of acceptable values see
https://bids-specification.readthedocs.io/en/latest/04-modality-specific-files/01-magnetic-resonance-imaging-data.html#anatomy-imaging-data
atropos_refine : bool
Enables or disables the whole ATROPOS sub-workflow
atropos_use_random_seed : bool
Whether ATROPOS should generate a random seed based on the
system's clock
atropos_model : tuple or None
Allows to specify a particular segmentation model, overwriting
the defaults based on ``bids_suffix``
name : str, optional
Workflow name (default: ``'n4_only_wf'``).
Inputs
------
in_files
List of input anatomical images to be bias corrected,
typically T1-weighted.
If a list of anatomical images is provided, 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 T1w as the first image.
Outputs
-------
out_file
:abbr:`INU (intensity non-uniformity)`-corrected ``in_files``
out_mask
Calculated brain mask
bias_corrected
Same as "out_file", provided for consistency with brain extraction
bias_image
The :abbr:`INU (intensity non-uniformity)` field estimated for each
input in ``in_files``
out_segm
Output segmentation by ATROPOS
out_tpms
Output :abbr:`TPMs (tissue probability maps)` by ATROPOS
"""
wf = pe.Workflow(name)
inputnode = pe.Node(
niu.IdentityInterface(fields=["in_files", "in_mask"]), name="inputnode"
)
outputnode = pe.Node(
niu.IdentityInterface(
fields=[
"out_file",
"out_mask",
"bias_corrected",
"bias_image",
"out_segm",
"out_tpms",
]
),
name="outputnode",
)
# Create brain mask
thr_brainmask = pe.Node(Binarize(thresh_low=2), name="binarize")
# 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=200,
),
n_procs=omp_nthreads,
name="inu_n4_final",
iterfield=["input_image"],
)
# Check ANTs version
try:
inu_n4_final.inputs.rescale_intensities = True
except ValueError:
warn(
"The installed ANTs version too old. Please consider upgrading to "
"2.1.0 or greater.",
DeprecationWarning,
)
# fmt: off
wf.connect([
(inputnode, inu_n4_final, [("in_files", "input_image")]),
(inputnode, thr_brainmask, [(("in_files", _pop), "in_file")]),
(thr_brainmask, outputnode, [("out_mask", "out_mask")]),
(inu_n4_final, outputnode, [("output_image", "out_file")]),
(inu_n4_final, outputnode, [("output_image", "bias_corrected")]),
(inu_n4_final, outputnode, [("bias_image", "bias_image")]),
])
# fmt: on
# If atropos refine, do in4 twice
if atropos_refine:
# Morphological dilation, radius=2
dil_brainmask = pe.Node(
ImageMath(operation="MD", op2="2"), name="dil_brainmask"
)
# Get largest connected component
get_brainmask = pe.Node(
ImageMath(operation="GetLargestComponent"), name="get_brainmask"
)
atropos_model = atropos_model or list(ATROPOS_MODELS[bids_suffix].values())
atropos_wf = init_atropos_wf(
use_random_seed=atropos_use_random_seed,
omp_nthreads=omp_nthreads,
mem_gb=mem_gb,
in_segmentation_model=atropos_model,
)
sel_wm = pe.Node(
niu.Select(index=atropos_model[-1] - 1),
name="sel_wm",
run_without_submitting=True,
)
inu_n4 = pe.MapNode(
N4BiasFieldCorrection(
dimension=3,
save_bias=False,
copy_header=True,
n_iterations=[50] * 4,
convergence_threshold=1e-7,
shrink_factor=4,
bspline_fitting_distance=200,
),
n_procs=omp_nthreads,
name="inu_n4",
iterfield=["input_image"],
)
# fmt: off
wf.connect([
(inputnode, inu_n4, [("in_files", "input_image")]),
(inu_n4, atropos_wf, [("output_image", "inputnode.in_files")]),
(thr_brainmask, atropos_wf, [("out_mask", "inputnode.in_mask")]),
(thr_brainmask, dil_brainmask, [("out_mask", "op1")]),
(dil_brainmask, get_brainmask, [("output_image", "op1")]),
(get_brainmask, atropos_wf, [
("output_image", "inputnode.in_mask_dilated"),
]),
(atropos_wf, sel_wm, [("outputnode.out_tpms", "inlist")]),
(sel_wm, inu_n4_final, [("out", "weight_image")]),
(atropos_wf, outputnode, [
("outputnode.out_segm", "out_segm"),
("outputnode.out_tpms", "out_tpms"),
]),
])
# fmt: on
return wf
def _select_labels(in_segm, labels):
from os import getcwd
import numpy as np
import nibabel as nb
from nipype.utils.filemanip import fname_presuffix
out_files = []
cwd = getcwd()
nii = nb.load(in_segm)
label_data = np.asanyarray(nii.dataobj).astype("uint8")
for label in labels:
newnii = nii.__class__(np.uint8(label_data == label), nii.affine, nii.header)
newnii.set_data_dtype("uint8")
out_file = fname_presuffix(in_segm, suffix="_class-%02d" % label, newpath=cwd)
newnii.to_filename(out_file)
out_files.append(out_file)
return out_files
def _conform_mask(in_mask, in_reference):
"""Ensures the mask headers make sense and match those of the T1w"""
from pathlib import Path
import numpy as np
import nibabel as nb
from nipype.utils.filemanip import fname_presuffix
ref = nb.load(in_reference)
nii = nb.load(in_mask)
hdr = nii.header.copy()
hdr.set_data_dtype("int16")
hdr.set_slope_inter(1, 0)
qform, qcode = ref.header.get_qform(coded=True)
if qcode is not None:
hdr.set_qform(qform, int(qcode))
sform, scode = ref.header.get_sform(coded=True)
if scode is not None:
hdr.set_sform(sform, int(scode))
if "_maths" in in_mask: # Cut the name at first _maths occurrence
ext = "".join(Path(in_mask).suffixes)
basename = Path(in_mask).name
in_mask = basename.split("_maths")[0] + ext
out_file = fname_presuffix(in_mask, suffix="_mask", newpath=str(Path()))
nii.__class__(
np.asanyarray(nii.dataobj).astype("int16"), ref.affine, hdr
).to_filename(out_file)
return out_file