def headmsk_wf(name='HeadMaskWorkflow', use_bet=True):
"""
Computes a head mask as in [Mortamet2009]_.
.. workflow::
from mriqc.workflows.anatomical import headmsk_wf
wf = headmsk_wf()
"""
has_dipy = False
try:
from dipy.denoise import nlmeans # noqa
has_dipy = True
except ImportError:
pass
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['in_file', 'in_segm']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['out_file']), name='outputnode')
if use_bet or not has_dipy:
# Alternative for when dipy is not installed
bet = pe.Node(fsl.BET(surfaces=True), name='fsl_bet')
workflow.connect([
(inputnode, bet, [('in_file', 'in_file')]),
(bet, outputnode, [('outskin_mask_file', 'out_file')])
])
else:
from nipype.interfaces.dipy import Denoise
enhance = pe.Node(niu.Function(
input_names=['in_file'], output_names=['out_file'], function=_enhance), name='Enhance')
estsnr = pe.Node(niu.Function(
input_names=['in_file', 'seg_file'], output_names=['out_snr'],
function=_estimate_snr), name='EstimateSNR')
denoise = pe.Node(Denoise(), name='Denoise')
gradient = pe.Node(niu.Function(
input_names=['in_file', 'snr'], output_names=['out_file'],
function=image_gradient), name='Grad')
thresh = pe.Node(niu.Function(
input_names=['in_file', 'in_segm'], output_names=['out_file'],
function=gradient_threshold), name='GradientThreshold')
workflow.connect([
(inputnode, estsnr, [('in_file', 'in_file'),
('in_segm', 'seg_file')]),
(estsnr, denoise, [('out_snr', 'snr')]),
(inputnode, enhance, [('in_file', 'in_file')]),
(enhance, denoise, [('out_file', 'in_file')]),
(estsnr, gradient, [('out_snr', 'snr')]),
(denoise, gradient, [('out_file', 'in_file')]),
(inputnode, thresh, [('in_segm', 'in_segm')]),
(gradient, thresh, [('out_file', 'in_file')]),
(thresh, outputnode, [('out_file', 'out_file')])
])
return workflow
def headmsk_wf(name='HeadMaskWorkflow'):
"""Computes a head mask as in [Mortamet2009]_."""
has_dipy = False
try:
from dipy.denoise import nlmeans
from nipype.interfaces.dipy import Denoise
has_dipy = True
except ImportError:
pass
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=['in_file', 'in_segm']),
name='inputnode')
outputnode = pe.Node(niu.IdentityInterface(fields=['out_file']),
name='outputnode')
if not has_dipy:
# Alternative for when dipy is not installed
bet = pe.Node(fsl.BET(surfaces=True), name='fsl_bet')
workflow.connect([(inputnode, bet, [('in_file', 'in_file')]),
(bet, outputnode, [('outskin_mask_file', 'out_file')
])])
return workflow
getwm = pe.Node(niu.Function(input_names=['in_file'],
output_names=['out_file'],
function=_get_wm),
name='GetWM')
denoise = pe.Node(Denoise(), name='Denoise')
gradient = pe.Node(niu.Function(input_names=['in_file'],
output_names=['out_file'],
function=image_gradient),
name='Grad')
thresh = pe.Node(niu.Function(input_names=['in_file'],
output_names=['out_file'],
function=gradient_threshold),
name='GradientThreshold')
workflow.connect([(inputnode, getwm, [('in_segm', 'in_file')]),
(inputnode, denoise, [('in_file', 'in_file')]),
(getwm, denoise, [('out_file', 'noise_mask')]),
(denoise, gradient, [('out_file', 'in_file')]),
(gradient, thresh, [('out_file', 'in_file')]),
(thresh, outputnode, [('out_file', 'out_file')])])
return workflow
def headmsk_wf(name="HeadMaskWorkflow"):
"""
Computes a head mask as in [Mortamet2009]_.
.. workflow::
from mriqc.testing import mock_config
from mriqc.workflows.anatomical import headmsk_wf
with mock_config():
wf = headmsk_wf()
"""
use_bet = config.workflow.headmask.upper() == "BET"
has_dipy = False
if not use_bet:
try:
from dipy.denoise import nlmeans # noqa
has_dipy = True
except ImportError:
pass
if not use_bet and not has_dipy:
raise RuntimeError(
"DIPY is not installed and ``config.workflow.headmask`` is not BET."
)
workflow = pe.Workflow(name=name)
inputnode = pe.Node(niu.IdentityInterface(fields=["in_file", "in_segm"]),
name="inputnode")
outputnode = pe.Node(niu.IdentityInterface(fields=["out_file"]),
name="outputnode")
if use_bet:
# Alternative for when dipy is not installed
bet = pe.Node(fsl.BET(surfaces=True), name="fsl_bet")
# fmt: off
workflow.connect([
(inputnode, bet, [("in_file", "in_file")]),
(bet, outputnode, [('outskin_mask_file', "out_file")]),
])
# fmt: on
else:
from nipype.interfaces.dipy import Denoise
enhance = pe.Node(
niu.Function(
input_names=["in_file"],
output_names=["out_file"],
function=_enhance,
),
name="Enhance",
)
estsnr = pe.Node(
niu.Function(
input_names=["in_file", "seg_file"],
output_names=["out_snr"],
function=_estimate_snr,
),
name="EstimateSNR",
)
denoise = pe.Node(Denoise(), name="Denoise")
gradient = pe.Node(
niu.Function(
input_names=["in_file", "snr", "sigma"],
output_names=["out_file"],
function=image_gradient,
),
name="Grad",
)
thresh = pe.Node(
niu.Function(
input_names=["in_file", "in_segm", "aniso", "thresh"],
output_names=["out_file"],
function=gradient_threshold,
),
name="GradientThreshold",
)
if config.workflow.species != "human":
calc_sigma = pe.Node(
niu.Function(
input_names=["in_file"],
output_names=["sigma"],
function=sigma_calc,
),
name="calc_sigma",
)
workflow.connect([
(inputnode, calc_sigma, [("in_file", "in_file")]),
(calc_sigma, gradient, [("sigma", "sigma")]),
])
thresh.inputs.aniso = True
thresh.inputs.thresh = 4.0
# fmt: off
workflow.connect([
(inputnode, estsnr, [("in_file", "in_file"),
("in_segm", "seg_file")]),
(estsnr, denoise, [("out_snr", "snr")]),
(inputnode, enhance, [("in_file", "in_file")]),
(enhance, denoise, [("out_file", "in_file")]),
(estsnr, gradient, [("out_snr", "snr")]),
(denoise, gradient, [("out_file", "in_file")]),
(inputnode, thresh, [("in_segm", "in_segm")]),
(gradient, thresh, [("out_file", "in_file")]),
(thresh, outputnode, [("out_file", "out_file")]),
])
# fmt: on
return workflow