def main():
parser = argparse.ArgumentParser()
parser.add_argument('-s', '--scan', type=str, help='/path/to/scan/file')
parser.add_argument('-m', '--mask', type=str, help='/path/to/mask/file')
parser.add_argument('-o',
'--output',
type=str,
help='/path/to/output/file')
args = parser.parse_args()
# Define variables
sequenceFn = args.scan
maskFn = args.mask
outFn = args.output
# Load the sequence
sequence, coords1 = mil.loadBOLD(sequenceFn)
# Load the mask
mask, coords2 = mil.loadBOLD(maskFn)
print("sequence", sequence.shape)
print("mask", mask.shape)
masking = ApplyMask()
masking.inputs.in_file = sequenceFn
masking.inputs.mask_file = maskFn
masking.inputs.out_file = outFn
masking.run()
def test_FASTRPT(monkeypatch, segments, reference, reference_mask):
""" test FAST with the two options for segments """
from nipype.interfaces.fsl.maths import ApplyMask
def _agg(objekt, runtime):
outputs = Bunch(
tissue_class_map=os.path.join(
datadir, 'testFASTRPT-tissue_class_map.nii.gz'),
tissue_class_files=[
os.path.join(datadir,
'testFASTRPT-tissue_class_files0.nii.gz'),
os.path.join(datadir,
'testFASTRPT-tissue_class_files1.nii.gz'),
os.path.join(datadir, 'testFASTRPT-tissue_class_files2.nii.gz')
])
return outputs
# Patch the _run_interface method
monkeypatch.setattr(FASTRPT, '_run_interface', _run_interface_mock)
monkeypatch.setattr(FASTRPT, 'aggregate_outputs', _agg)
brain = ApplyMask(in_file=reference,
mask_file=reference_mask).run().outputs.out_file
fast_rpt = FASTRPT(in_files=brain,
generate_report=True,
no_bias=True,
probability_maps=True,
segments=segments,
out_basename='test')
_smoke_test_report(fast_rpt,
'testFAST_%ssegments.svg' % ('no' * int(not segments)))
def test_ApplyMask_outputs():
output_map = dict(out_file=dict(), )
outputs = ApplyMask.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_FASTRPT(monkeypatch, segments, reference, reference_mask):
""" test FAST with the two options for segments """
from nipype.interfaces.fsl.maths import ApplyMask
def _agg(objekt, runtime):
outputs = objekt.output_spec()
outputs.out_report = os.path.join(runtime.cwd,
objekt.inputs.out_report)
outputs.tissue_class_map = os.path.join(
datadir, "testFASTRPT-tissue_class_map.nii.gz")
outputs.tissue_class_files = [
os.path.join(datadir, "testFASTRPT-tissue_class_files0.nii.gz"),
os.path.join(datadir, "testFASTRPT-tissue_class_files1.nii.gz"),
os.path.join(datadir, "testFASTRPT-tissue_class_files2.nii.gz"),
]
return outputs
# Patch the _run_interface method
monkeypatch.setattr(FASTRPT, "_run_interface", _run_interface_mock)
monkeypatch.setattr(FASTRPT, "aggregate_outputs", _agg)
brain = (pe.Node(ApplyMask(in_file=reference, mask_file=reference_mask),
name="brain").run().outputs.out_file)
fast_rpt = FASTRPT(
in_files=brain,
generate_report=True,
no_bias=True,
probability_maps=True,
segments=segments,
out_basename="test",
)
_smoke_test_report(fast_rpt,
"testFAST_%ssegments.svg" % ("no" * int(not segments)))
def test_ApplyMask_outputs():
output_map = dict(out_file=dict(),
)
outputs = ApplyMask.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_ApplyMask_inputs():
input_map = dict(
args=dict(argstr='%s', ),
environ=dict(
nohash=True,
usedefault=True,
),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
in_file=dict(
argstr='%s',
mandatory=True,
position=2,
),
internal_datatype=dict(
argstr='-dt %s',
position=1,
),
mask_file=dict(
argstr='-mas %s',
mandatory=True,
position=4,
),
nan2zeros=dict(
argstr='-nan',
position=3,
),
out_file=dict(
argstr='%s',
genfile=True,
hash_files=False,
position=-2,
),
output_datatype=dict(
argstr='-odt %s',
position=-1,
),
output_type=dict(),
terminal_output=dict(
mandatory=True,
nohash=True,
),
)
inputs = ApplyMask.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_ApplyMask_inputs():
input_map = dict(ignore_exception=dict(nohash=True,
usedefault=True,
),
nan2zeros=dict(position=3,
argstr='-nan',
),
out_file=dict(hash_files=False,
genfile=True,
position=-2,
argstr='%s',
),
args=dict(argstr='%s',
),
internal_datatype=dict(position=1,
argstr='-dt %s',
),
terminal_output=dict(mandatory=True,
nohash=True,
),
environ=dict(nohash=True,
usedefault=True,
),
in_file=dict(position=2,
mandatory=True,
argstr='%s',
),
mask_file=dict(position=4,
mandatory=True,
argstr='-mas %s',
),
output_type=dict(),
output_datatype=dict(position=-1,
argstr='-odt %s',
),
)
inputs = ApplyMask.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def init_brain_extraction_wf(name='brain_extraction_wf',
in_template='OASIS',
use_float=True,
normalization_quality='precise',
omp_nthreads=None,
mem_gb=3.0,
modality='T1',
atropos_refine=True,
atropos_use_random_seed=True,
atropos_model=None):
"""
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::
:graph2use: orig
:simple_form: yes
from niworkflows.anat import init_brain_extraction_wf
wf = init_brain_extraction_wf()
**Parameters**
in_template : str
Name of the skull-stripping template ('OASIS', '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
modality : str
Sequence type of the first input image ('T1', 'T2', or 'FLAIR')
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 ``modality``
name : str, optional
Workflow name (default: antsBrainExtraction)
**Inputs**
in_files
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
(optional) Mask used for registration to limit the metric
computation to a specific region.
**Outputs**
bias_corrected
The ``in_files`` input images, after :abbr:`INU (intensity non-uniformity)`
correction.
out_mask
Calculated brain mask
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)
template_path = None
if in_template in TEMPLATE_MAP:
template_path = get_dataset(in_template)
else:
template_path = in_template
mod = ('%sw' % modality[:2].upper()
if modality.upper().startswith('T') else modality.upper())
# Append template modality
potential_targets = list(Path(template_path).glob('*_%s.nii.gz' % mod))
if not potential_targets:
raise ValueError('No %s template was found under "%s".' %
(mod, template_path))
tpl_target_path = str(potential_targets[0])
target_basename = '_'.join(tpl_target_path.split('_')[:-1])
# Get probabilistic brain mask if available
tpl_mask_path = '%s_class-brainmask_probtissue.nii.gz' % target_basename
# Fall-back to a binary mask just in case
if not os.path.exists(tpl_mask_path):
tpl_mask_path = '%s_brainmask.nii.gz' % target_basename
if not os.path.exists(tpl_mask_path):
raise ValueError(
'Probability map for the brain mask associated to this template '
'"%s" not found.' % tpl_mask_path)
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 = '%s_label-BrainCerebellumRegistration_roi.nii.gz' % target_basename
if os.path.exists(tpl_regmask_path):
inputnode.inputs.in_mask = tpl_regmask_path
outputnode = pe.Node(niu.IdentityInterface(
fields=['bias_corrected', 'out_mask', 'bias_image', 'out_segm']),
name='outputnode')
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=True,
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'])
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', 0.2),
transform=('Affine', 0.1),
search_factor=(20, 0.12),
principal_axes=False,
convergence=(10, 1e-6, 10),
verbose=True),
name='init_aff',
n_procs=omp_nthreads)
if parseversion(Registration().version) > Version('2.2.0'):
init_aff.inputs.search_grid = (40, (0, 40, 40))
# Set up spatial normalization
norm = pe.Node(Registration(from_file=pkgr_fn(
'niworkflows.data', 'antsBrainExtraction_%s.json' %
normalization_quality)),
name='norm',
n_procs=omp_nthreads,
mem_gb=mem_gb)
norm.inputs.float = use_float
fixed_mask_trait = 'fixed_image_mask'
if parseversion(Registration().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 = 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')
# Apply mask
apply_mask = pe.MapNode(ApplyMask(),
iterfield=['in_file'],
name='apply_mask')
wf.connect([
(inputnode, trunc, [('in_files', 'op1')]),
(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')]),
(inu_n4, lap_target, [(('output_image', _pop), 'op1')]),
(res_tmpl, init_aff, [('output_image', 'fixed_image')]),
(res_target, init_aff, [('output_image', 'moving_image')]),
(inu_n4, mrg_target, [('output_image', 'in1')]),
(lap_tmpl, mrg_tmpl, [('output_image', 'in2')]),
(lap_target, mrg_target, [('output_image', 'in2')]),
(init_aff, norm, [('output_transform', 'initial_moving_transform')]),
(mrg_tmpl, norm, [('out', 'fixed_image')]),
(mrg_target, norm, [('out', 'moving_image')]),
(norm, map_brainmask, [('reverse_invert_flags',
'invert_transform_flags'),
('reverse_transforms', 'transforms')]),
(map_brainmask, thr_brainmask, [('output_image', 'input_image')]),
(thr_brainmask, dil_brainmask, [('output_image', 'op1')]),
(dil_brainmask, get_brainmask, [('output_image', 'op1')]),
(inu_n4, apply_mask, [('output_image', 'in_file')]),
(get_brainmask, apply_mask, [('output_image', 'mask_file')]),
(get_brainmask, outputnode, [('output_image', 'out_mask')]),
(apply_mask, outputnode, [('out_file', 'bias_corrected')]),
(inu_n4, outputnode, [('bias_image', 'bias_image')]),
])
if atropos_refine:
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
or list(ATROPOS_MODELS[modality].values()))
wf.disconnect([
(get_brainmask, outputnode, [('output_image', 'out_mask')]),
(get_brainmask, apply_mask, [('output_image', 'mask_file')]),
])
wf.connect([
(inu_n4, atropos_wf, [('output_image', 'inputnode.in_files')]),
(get_brainmask, atropos_wf, [('output_image', 'inputnode.in_mask')
]),
(atropos_wf, outputnode, [('outputnode.out_mask', 'out_mask')]),
(atropos_wf, apply_mask, [('outputnode.out_mask', 'mask_file')]),
(atropos_wf, outputnode, [('outputnode.out_segm', 'out_segm'),
('outputnode.out_tpms', 'out_tpms')])
])
return wf
def Lesion_extractor(
name='Lesion_Extractor',
wf_name='Test',
base_dir='/homes_unix/alaurent/',
input_dir=None,
subjects=None,
main=None,
acc=None,
atlas='/homes_unix/alaurent/cbstools-public-master/atlases/brain-segmentation-prior3.0/brain-atlas-quant-3.0.8.txt'
):
wf = Workflow(wf_name)
wf.base_dir = base_dir
#file = open(subjects,"r")
#subjects = file.read().split("\n")
#file.close()
# Subject List
subjectList = Node(IdentityInterface(fields=['subject_id'],
mandatory_inputs=True),
name="subList")
subjectList.iterables = ('subject_id', [
sub for sub in subjects if sub != '' and sub != '\n'
])
# T1w and FLAIR
scanList = Node(DataGrabber(infields=['subject_id'],
outfields=['T1', 'FLAIR']),
name="scanList")
scanList.inputs.base_directory = input_dir
scanList.inputs.ignore_exception = False
scanList.inputs.raise_on_empty = True
scanList.inputs.sort_filelist = True
#scanList.inputs.template = '%s/%s.nii'
#scanList.inputs.template_args = {'T1': [['subject_id','T1*']],
# 'FLAIR': [['subject_id','FLAIR*']]}
scanList.inputs.template = '%s/anat/%s'
scanList.inputs.template_args = {
'T1': [['subject_id', '*_T1w.nii.gz']],
'FLAIR': [['subject_id', '*_FLAIR.nii.gz']]
}
wf.connect(subjectList, "subject_id", scanList, "subject_id")
# # T1w and FLAIR
# dg = Node(DataGrabber(outfields=['T1', 'FLAIR']), name="T1wFLAIR")
# dg.inputs.base_directory = "/homes_unix/alaurent/LesionPipeline"
# dg.inputs.template = "%s/NIFTI/*.nii.gz"
# dg.inputs.template_args['T1']=[['7']]
# dg.inputs.template_args['FLAIR']=[['9']]
# dg.inputs.sort_filelist=True
# Reorient Volume
T1Conv = Node(Reorient2Std(), name="ReorientVolume")
T1Conv.inputs.ignore_exception = False
T1Conv.inputs.terminal_output = 'none'
T1Conv.inputs.out_file = "T1_reoriented.nii.gz"
wf.connect(scanList, "T1", T1Conv, "in_file")
# Reorient Volume (2)
T2flairConv = Node(Reorient2Std(), name="ReorientVolume2")
T2flairConv.inputs.ignore_exception = False
T2flairConv.inputs.terminal_output = 'none'
T2flairConv.inputs.out_file = "FLAIR_reoriented.nii.gz"
wf.connect(scanList, "FLAIR", T2flairConv, "in_file")
# N3 Correction
T1NUC = Node(N4BiasFieldCorrection(), name="N3Correction")
T1NUC.inputs.dimension = 3
T1NUC.inputs.environ = {'NSLOTS': '1'}
T1NUC.inputs.ignore_exception = False
T1NUC.inputs.num_threads = 1
T1NUC.inputs.save_bias = False
T1NUC.inputs.terminal_output = 'none'
wf.connect(T1Conv, "out_file", T1NUC, "input_image")
# N3 Correction (2)
T2flairNUC = Node(N4BiasFieldCorrection(), name="N3Correction2")
T2flairNUC.inputs.dimension = 3
T2flairNUC.inputs.environ = {'NSLOTS': '1'}
T2flairNUC.inputs.ignore_exception = False
T2flairNUC.inputs.num_threads = 1
T2flairNUC.inputs.save_bias = False
T2flairNUC.inputs.terminal_output = 'none'
wf.connect(T2flairConv, "out_file", T2flairNUC, "input_image")
'''
#####################
### PRE-NORMALIZE ###
#####################
To make sure there's no outlier values (negative, or really high) to offset the initialization steps
'''
# Intensity Range Normalization
getMaxT1NUC = Node(ImageStats(op_string='-r'), name="getMaxT1NUC")
wf.connect(T1NUC, 'output_image', getMaxT1NUC, 'in_file')
T1NUCirn = Node(AbcImageMaths(), name="IntensityNormalization")
T1NUCirn.inputs.op_string = "-div"
T1NUCirn.inputs.out_file = "normT1.nii.gz"
wf.connect(T1NUC, 'output_image', T1NUCirn, 'in_file')
wf.connect(getMaxT1NUC, ('out_stat', getElementFromList, 1), T1NUCirn,
"op_value")
# Intensity Range Normalization (2)
getMaxT2NUC = Node(ImageStats(op_string='-r'), name="getMaxT2")
wf.connect(T2flairNUC, 'output_image', getMaxT2NUC, 'in_file')
T2NUCirn = Node(AbcImageMaths(), name="IntensityNormalization2")
T2NUCirn.inputs.op_string = "-div"
T2NUCirn.inputs.out_file = "normT2.nii.gz"
wf.connect(T2flairNUC, 'output_image', T2NUCirn, 'in_file')
wf.connect(getMaxT2NUC, ('out_stat', getElementFromList, 1), T2NUCirn,
"op_value")
'''
########################
#### COREGISTRATION ####
########################
'''
# Optimized Automated Registration
T2flairCoreg = Node(FLIRT(), name="OptimizedAutomatedRegistration")
T2flairCoreg.inputs.output_type = 'NIFTI_GZ'
wf.connect(T2NUCirn, "out_file", T2flairCoreg, "in_file")
wf.connect(T1NUCirn, "out_file", T2flairCoreg, "reference")
'''
#########################
#### SKULL-STRIPPING ####
#########################
'''
# SPECTRE
T1ss = Node(BET(), name="SPECTRE")
T1ss.inputs.frac = 0.45 #0.4
T1ss.inputs.mask = True
T1ss.inputs.outline = True
T1ss.inputs.robust = True
wf.connect(T1NUCirn, "out_file", T1ss, "in_file")
# Image Calculator
T2ss = Node(ApplyMask(), name="ImageCalculator")
wf.connect(T1ss, "mask_file", T2ss, "mask_file")
wf.connect(T2flairCoreg, "out_file", T2ss, "in_file")
'''
####################################
#### 2nd LAYER OF N3 CORRECTION ####
####################################
This time without the skull: there were some significant amounts of inhomogeneities leftover.
'''
# N3 Correction (3)
T1ssNUC = Node(N4BiasFieldCorrection(), name="N3Correction3")
T1ssNUC.inputs.dimension = 3
T1ssNUC.inputs.environ = {'NSLOTS': '1'}
T1ssNUC.inputs.ignore_exception = False
T1ssNUC.inputs.num_threads = 1
T1ssNUC.inputs.save_bias = False
T1ssNUC.inputs.terminal_output = 'none'
wf.connect(T1ss, "out_file", T1ssNUC, "input_image")
# N3 Correction (4)
T2ssNUC = Node(N4BiasFieldCorrection(), name="N3Correction4")
T2ssNUC.inputs.dimension = 3
T2ssNUC.inputs.environ = {'NSLOTS': '1'}
T2ssNUC.inputs.ignore_exception = False
T2ssNUC.inputs.num_threads = 1
T2ssNUC.inputs.save_bias = False
T2ssNUC.inputs.terminal_output = 'none'
wf.connect(T2ss, "out_file", T2ssNUC, "input_image")
'''
####################################
#### NORMALIZE FOR MGDM ####
####################################
This normalization is a bit aggressive: only useful to have a
cropped dynamic range into MGDM, but possibly harmful to further
processing, so the unprocessed images are passed to the subsequent steps.
'''
# Intensity Range Normalization
getMaxT1ssNUC = Node(ImageStats(op_string='-r'), name="getMaxT1ssNUC")
wf.connect(T1ssNUC, 'output_image', getMaxT1ssNUC, 'in_file')
T1ssNUCirn = Node(AbcImageMaths(), name="IntensityNormalization3")
T1ssNUCirn.inputs.op_string = "-div"
T1ssNUCirn.inputs.out_file = "normT1ss.nii.gz"
wf.connect(T1ssNUC, 'output_image', T1ssNUCirn, 'in_file')
wf.connect(getMaxT1ssNUC, ('out_stat', getElementFromList, 1), T1ssNUCirn,
"op_value")
# Intensity Range Normalization (2)
getMaxT2ssNUC = Node(ImageStats(op_string='-r'), name="getMaxT2ssNUC")
wf.connect(T2ssNUC, 'output_image', getMaxT2ssNUC, 'in_file')
T2ssNUCirn = Node(AbcImageMaths(), name="IntensityNormalization4")
T2ssNUCirn.inputs.op_string = "-div"
T2ssNUCirn.inputs.out_file = "normT2ss.nii.gz"
wf.connect(T2ssNUC, 'output_image', T2ssNUCirn, 'in_file')
wf.connect(getMaxT2ssNUC, ('out_stat', getElementFromList, 1), T2ssNUCirn,
"op_value")
'''
####################################
#### ESTIMATE CSF PV ####
####################################
Here we try to get a better handle on CSF voxels to help the segmentation step
'''
# Recursive Ridge Diffusion
CSF_pv = Node(RecursiveRidgeDiffusion(), name='estimate_CSF_pv')
CSF_pv.plugin_args = {'sbatch_args': '--mem 6000'}
CSF_pv.inputs.ridge_intensities = "dark"
CSF_pv.inputs.ridge_filter = "2D"
CSF_pv.inputs.orientation = "undefined"
CSF_pv.inputs.ang_factor = 1.0
CSF_pv.inputs.min_scale = 0
CSF_pv.inputs.max_scale = 3
CSF_pv.inputs.propagation_model = "diffusion"
CSF_pv.inputs.diffusion_factor = 0.5
CSF_pv.inputs.similarity_scale = 0.1
CSF_pv.inputs.neighborhood_size = 4
CSF_pv.inputs.max_iter = 100
CSF_pv.inputs.max_diff = 0.001
CSF_pv.inputs.save_data = True
wf.connect(
subjectList,
('subject_id', createOutputDir, wf.base_dir, wf.name, CSF_pv.name),
CSF_pv, 'output_dir')
wf.connect(T1ssNUCirn, 'out_file', CSF_pv, 'input_image')
'''
####################################
#### MGDM ####
####################################
'''
# Multi-contrast Brain Segmentation
MGDM = Node(MGDMSegmentation(), name='MGDM')
MGDM.plugin_args = {'sbatch_args': '--mem 7000'}
MGDM.inputs.contrast_type1 = "Mprage3T"
MGDM.inputs.contrast_type2 = "FLAIR3T"
MGDM.inputs.contrast_type3 = "PVDURA"
MGDM.inputs.save_data = True
MGDM.inputs.atlas_file = atlas
wf.connect(
subjectList,
('subject_id', createOutputDir, wf.base_dir, wf.name, MGDM.name), MGDM,
'output_dir')
wf.connect(T1ssNUCirn, 'out_file', MGDM, 'contrast_image1')
wf.connect(T2ssNUCirn, 'out_file', MGDM, 'contrast_image2')
wf.connect(CSF_pv, 'ridge_pv', MGDM, 'contrast_image3')
# Enhance Region Contrast
ERC = Node(EnhanceRegionContrast(), name='ERC')
ERC.plugin_args = {'sbatch_args': '--mem 7000'}
ERC.inputs.enhanced_region = "crwm"
ERC.inputs.contrast_background = "crgm"
ERC.inputs.partial_voluming_distance = 2.0
ERC.inputs.save_data = True
ERC.inputs.atlas_file = atlas
wf.connect(subjectList,
('subject_id', createOutputDir, wf.base_dir, wf.name, ERC.name),
ERC, 'output_dir')
wf.connect(T1ssNUC, 'output_image', ERC, 'intensity_image')
wf.connect(MGDM, 'segmentation', ERC, 'segmentation_image')
wf.connect(MGDM, 'distance', ERC, 'levelset_boundary_image')
# Enhance Region Contrast (2)
ERC2 = Node(EnhanceRegionContrast(), name='ERC2')
ERC2.plugin_args = {'sbatch_args': '--mem 7000'}
ERC2.inputs.enhanced_region = "crwm"
ERC2.inputs.contrast_background = "crgm"
ERC2.inputs.partial_voluming_distance = 2.0
ERC2.inputs.save_data = True
ERC2.inputs.atlas_file = atlas
wf.connect(
subjectList,
('subject_id', createOutputDir, wf.base_dir, wf.name, ERC2.name), ERC2,
'output_dir')
wf.connect(T2ssNUC, 'output_image', ERC2, 'intensity_image')
wf.connect(MGDM, 'segmentation', ERC2, 'segmentation_image')
wf.connect(MGDM, 'distance', ERC2, 'levelset_boundary_image')
# Define Multi-Region Priors
DMRP = Node(DefineMultiRegionPriors(), name='DefineMultRegPriors')
DMRP.plugin_args = {'sbatch_args': '--mem 6000'}
#DMRP.inputs.defined_region = "ventricle-horns"
#DMRP.inputs.definition_method = "closest-distance"
DMRP.inputs.distance_offset = 3.0
DMRP.inputs.save_data = True
DMRP.inputs.atlas_file = atlas
wf.connect(
subjectList,
('subject_id', createOutputDir, wf.base_dir, wf.name, DMRP.name), DMRP,
'output_dir')
wf.connect(MGDM, 'segmentation', DMRP, 'segmentation_image')
wf.connect(MGDM, 'distance', DMRP, 'levelset_boundary_image')
'''
###############################################
#### REMOVE VENTRICLE POSTERIOR ####
###############################################
Due to topology constraints, the ventricles are often not fully segmented:
here add back all ventricle voxels from the posterior probability (without the topology constraints)
'''
# Posterior label
PostLabel = Node(Split(), name='PosteriorLabel')
PostLabel.inputs.dimension = "t"
wf.connect(MGDM, 'labels', PostLabel, 'in_file')
# Posterior proba
PostProba = Node(Split(), name='PosteriorProba')
PostProba.inputs.dimension = "t"
wf.connect(MGDM, 'memberships', PostProba, 'in_file')
# Threshold binary mask : ventricle label part 1
VentLabel1 = Node(Threshold(), name="VentricleLabel1")
VentLabel1.inputs.thresh = 10.5
VentLabel1.inputs.direction = "below"
wf.connect(PostLabel, ("out_files", getFirstElement), VentLabel1,
"in_file")
# Threshold binary mask : ventricle label part 2
VentLabel2 = Node(Threshold(), name="VentricleLabel2")
VentLabel2.inputs.thresh = 13.5
VentLabel2.inputs.direction = "above"
wf.connect(VentLabel1, "out_file", VentLabel2, "in_file")
# Image calculator : ventricle proba
VentProba = Node(ImageMaths(), name="VentricleProba")
VentProba.inputs.op_string = "-mul"
VentProba.inputs.out_file = "ventproba.nii.gz"
wf.connect(PostProba, ("out_files", getFirstElement), VentProba, "in_file")
wf.connect(VentLabel2, "out_file", VentProba, "in_file2")
# Image calculator : remove inter ventricles
RmInterVent = Node(ImageMaths(), name="RemoveInterVent")
RmInterVent.inputs.op_string = "-sub"
RmInterVent.inputs.out_file = "rmintervent.nii.gz"
wf.connect(ERC, "region_pv", RmInterVent, "in_file")
wf.connect(DMRP, "inter_ventricular_pv", RmInterVent, "in_file2")
# Image calculator : add horns
AddHorns = Node(ImageMaths(), name="AddHorns")
AddHorns.inputs.op_string = "-add"
AddHorns.inputs.out_file = "rmvent.nii.gz"
wf.connect(RmInterVent, "out_file", AddHorns, "in_file")
wf.connect(DMRP, "ventricular_horns_pv", AddHorns, "in_file2")
# Image calculator : remove ventricles
RmVent = Node(ImageMaths(), name="RemoveVentricles")
RmVent.inputs.op_string = "-sub"
RmVent.inputs.out_file = "rmvent.nii.gz"
wf.connect(AddHorns, "out_file", RmVent, "in_file")
wf.connect(VentProba, "out_file", RmVent, "in_file2")
# Image calculator : remove internal capsule
RmIC = Node(ImageMaths(), name="RemoveInternalCap")
RmIC.inputs.op_string = "-sub"
RmIC.inputs.out_file = "rmic.nii.gz"
wf.connect(RmVent, "out_file", RmIC, "in_file")
wf.connect(DMRP, "internal_capsule_pv", RmIC, "in_file2")
# Intensity Range Normalization (3)
getMaxRmIC = Node(ImageStats(op_string='-r'), name="getMaxRmIC")
wf.connect(RmIC, 'out_file', getMaxRmIC, 'in_file')
RmICirn = Node(AbcImageMaths(), name="IntensityNormalization5")
RmICirn.inputs.op_string = "-div"
RmICirn.inputs.out_file = "normRmIC.nii.gz"
wf.connect(RmIC, 'out_file', RmICirn, 'in_file')
wf.connect(getMaxRmIC, ('out_stat', getElementFromList, 1), RmICirn,
"op_value")
# Probability To Levelset : WM orientation
WM_Orient = Node(ProbabilityToLevelset(), name='WM_Orientation')
WM_Orient.plugin_args = {'sbatch_args': '--mem 6000'}
WM_Orient.inputs.save_data = True
wf.connect(
subjectList,
('subject_id', createOutputDir, wf.base_dir, wf.name, WM_Orient.name),
WM_Orient, 'output_dir')
wf.connect(RmICirn, 'out_file', WM_Orient, 'probability_image')
# Recursive Ridge Diffusion : PVS in WM only
WM_pvs = Node(RecursiveRidgeDiffusion(), name='PVS_in_WM')
WM_pvs.plugin_args = {'sbatch_args': '--mem 6000'}
WM_pvs.inputs.ridge_intensities = "bright"
WM_pvs.inputs.ridge_filter = "1D"
WM_pvs.inputs.orientation = "orthogonal"
WM_pvs.inputs.ang_factor = 1.0
WM_pvs.inputs.min_scale = 0
WM_pvs.inputs.max_scale = 3
WM_pvs.inputs.propagation_model = "diffusion"
WM_pvs.inputs.diffusion_factor = 1.0
WM_pvs.inputs.similarity_scale = 1.0
WM_pvs.inputs.neighborhood_size = 2
WM_pvs.inputs.max_iter = 100
WM_pvs.inputs.max_diff = 0.001
WM_pvs.inputs.save_data = True
wf.connect(
subjectList,
('subject_id', createOutputDir, wf.base_dir, wf.name, WM_pvs.name),
WM_pvs, 'output_dir')
wf.connect(ERC, 'background_proba', WM_pvs, 'input_image')
wf.connect(WM_Orient, 'levelset', WM_pvs, 'surface_levelset')
wf.connect(RmICirn, 'out_file', WM_pvs, 'loc_prior')
# Extract Lesions : extract WM PVS
extract_WM_pvs = Node(LesionExtraction(), name='ExtractPVSfromWM')
extract_WM_pvs.plugin_args = {'sbatch_args': '--mem 6000'}
extract_WM_pvs.inputs.gm_boundary_partial_vol_dist = 1.0
extract_WM_pvs.inputs.csf_boundary_partial_vol_dist = 3.0
extract_WM_pvs.inputs.lesion_clust_dist = 1.0
extract_WM_pvs.inputs.prob_min_thresh = 0.1
extract_WM_pvs.inputs.prob_max_thresh = 0.33
extract_WM_pvs.inputs.small_lesion_size = 4.0
extract_WM_pvs.inputs.save_data = True
extract_WM_pvs.inputs.atlas_file = atlas
wf.connect(subjectList, ('subject_id', createOutputDir, wf.base_dir,
wf.name, extract_WM_pvs.name), extract_WM_pvs,
'output_dir')
wf.connect(WM_pvs, 'propagation', extract_WM_pvs, 'probability_image')
wf.connect(MGDM, 'segmentation', extract_WM_pvs, 'segmentation_image')
wf.connect(MGDM, 'distance', extract_WM_pvs, 'levelset_boundary_image')
wf.connect(RmICirn, 'out_file', extract_WM_pvs, 'location_prior_image')
'''
2nd branch
'''
# Image calculator : internal capsule witout ventricules
ICwoVent = Node(ImageMaths(), name="ICWithoutVentricules")
ICwoVent.inputs.op_string = "-sub"
ICwoVent.inputs.out_file = "icwovent.nii.gz"
wf.connect(DMRP, "internal_capsule_pv", ICwoVent, "in_file")
wf.connect(DMRP, "inter_ventricular_pv", ICwoVent, "in_file2")
# Image calculator : remove ventricles IC
RmVentIC = Node(ImageMaths(), name="RmVentIC")
RmVentIC.inputs.op_string = "-sub"
RmVentIC.inputs.out_file = "RmVentIC.nii.gz"
wf.connect(ICwoVent, "out_file", RmVentIC, "in_file")
wf.connect(VentProba, "out_file", RmVentIC, "in_file2")
# Intensity Range Normalization (4)
getMaxRmVentIC = Node(ImageStats(op_string='-r'), name="getMaxRmVentIC")
wf.connect(RmVentIC, 'out_file', getMaxRmVentIC, 'in_file')
RmVentICirn = Node(AbcImageMaths(), name="IntensityNormalization6")
RmVentICirn.inputs.op_string = "-div"
RmVentICirn.inputs.out_file = "normRmVentIC.nii.gz"
wf.connect(RmVentIC, 'out_file', RmVentICirn, 'in_file')
wf.connect(getMaxRmVentIC, ('out_stat', getElementFromList, 1),
RmVentICirn, "op_value")
# Probability To Levelset : IC orientation
IC_Orient = Node(ProbabilityToLevelset(), name='IC_Orientation')
IC_Orient.plugin_args = {'sbatch_args': '--mem 6000'}
IC_Orient.inputs.save_data = True
wf.connect(
subjectList,
('subject_id', createOutputDir, wf.base_dir, wf.name, IC_Orient.name),
IC_Orient, 'output_dir')
wf.connect(RmVentICirn, 'out_file', IC_Orient, 'probability_image')
# Recursive Ridge Diffusion : PVS in IC only
IC_pvs = Node(RecursiveRidgeDiffusion(), name='RecursiveRidgeDiffusion2')
IC_pvs.plugin_args = {'sbatch_args': '--mem 6000'}
IC_pvs.inputs.ridge_intensities = "bright"
IC_pvs.inputs.ridge_filter = "1D"
IC_pvs.inputs.orientation = "undefined"
IC_pvs.inputs.ang_factor = 1.0
IC_pvs.inputs.min_scale = 0
IC_pvs.inputs.max_scale = 3
IC_pvs.inputs.propagation_model = "diffusion"
IC_pvs.inputs.diffusion_factor = 1.0
IC_pvs.inputs.similarity_scale = 1.0
IC_pvs.inputs.neighborhood_size = 2
IC_pvs.inputs.max_iter = 100
IC_pvs.inputs.max_diff = 0.001
IC_pvs.inputs.save_data = True
wf.connect(
subjectList,
('subject_id', createOutputDir, wf.base_dir, wf.name, IC_pvs.name),
IC_pvs, 'output_dir')
wf.connect(ERC, 'background_proba', IC_pvs, 'input_image')
wf.connect(IC_Orient, 'levelset', IC_pvs, 'surface_levelset')
wf.connect(RmVentICirn, 'out_file', IC_pvs, 'loc_prior')
# Extract Lesions : extract IC PVS
extract_IC_pvs = Node(LesionExtraction(), name='ExtractPVSfromIC')
extract_IC_pvs.plugin_args = {'sbatch_args': '--mem 6000'}
extract_IC_pvs.inputs.gm_boundary_partial_vol_dist = 1.0
extract_IC_pvs.inputs.csf_boundary_partial_vol_dist = 4.0
extract_IC_pvs.inputs.lesion_clust_dist = 1.0
extract_IC_pvs.inputs.prob_min_thresh = 0.25
extract_IC_pvs.inputs.prob_max_thresh = 0.5
extract_IC_pvs.inputs.small_lesion_size = 4.0
extract_IC_pvs.inputs.save_data = True
extract_IC_pvs.inputs.atlas_file = atlas
wf.connect(subjectList, ('subject_id', createOutputDir, wf.base_dir,
wf.name, extract_IC_pvs.name), extract_IC_pvs,
'output_dir')
wf.connect(IC_pvs, 'propagation', extract_IC_pvs, 'probability_image')
wf.connect(MGDM, 'segmentation', extract_IC_pvs, 'segmentation_image')
wf.connect(MGDM, 'distance', extract_IC_pvs, 'levelset_boundary_image')
wf.connect(RmVentICirn, 'out_file', extract_IC_pvs, 'location_prior_image')
'''
3rd branch
'''
# Image calculator :
RmInter = Node(ImageMaths(), name="RemoveInterVentricules")
RmInter.inputs.op_string = "-sub"
RmInter.inputs.out_file = "rminter.nii.gz"
wf.connect(ERC2, 'region_pv', RmInter, "in_file")
wf.connect(DMRP, "inter_ventricular_pv", RmInter, "in_file2")
# Image calculator :
AddVentHorns = Node(ImageMaths(), name="AddVentHorns")
AddVentHorns.inputs.op_string = "-add"
AddVentHorns.inputs.out_file = "rminter.nii.gz"
wf.connect(RmInter, 'out_file', AddVentHorns, "in_file")
wf.connect(DMRP, "ventricular_horns_pv", AddVentHorns, "in_file2")
# Intensity Range Normalization (5)
getMaxAddVentHorns = Node(ImageStats(op_string='-r'),
name="getMaxAddVentHorns")
wf.connect(AddVentHorns, 'out_file', getMaxAddVentHorns, 'in_file')
AddVentHornsirn = Node(AbcImageMaths(), name="IntensityNormalization7")
AddVentHornsirn.inputs.op_string = "-div"
AddVentHornsirn.inputs.out_file = "normAddVentHorns.nii.gz"
wf.connect(AddVentHorns, 'out_file', AddVentHornsirn, 'in_file')
wf.connect(getMaxAddVentHorns, ('out_stat', getElementFromList, 1),
AddVentHornsirn, "op_value")
# Extract Lesions : extract White Matter Hyperintensities
extract_WMH = Node(LesionExtraction(), name='Extract_WMH')
extract_WMH.plugin_args = {'sbatch_args': '--mem 6000'}
extract_WMH.inputs.gm_boundary_partial_vol_dist = 1.0
extract_WMH.inputs.csf_boundary_partial_vol_dist = 2.0
extract_WMH.inputs.lesion_clust_dist = 1.0
extract_WMH.inputs.prob_min_thresh = 0.84
extract_WMH.inputs.prob_max_thresh = 0.84
extract_WMH.inputs.small_lesion_size = 4.0
extract_WMH.inputs.save_data = True
extract_WMH.inputs.atlas_file = atlas
wf.connect(subjectList, ('subject_id', createOutputDir, wf.base_dir,
wf.name, extract_WMH.name), extract_WMH,
'output_dir')
wf.connect(ERC2, 'background_proba', extract_WMH, 'probability_image')
wf.connect(MGDM, 'segmentation', extract_WMH, 'segmentation_image')
wf.connect(MGDM, 'distance', extract_WMH, 'levelset_boundary_image')
wf.connect(AddVentHornsirn, 'out_file', extract_WMH,
'location_prior_image')
#===========================================================================
# extract_WMH2 = extract_WMH.clone(name='Extract_WMH2')
# extract_WMH2.inputs.gm_boundary_partial_vol_dist = 2.0
# wf.connect(subjectList,('subject_id',createOutputDir,wf.base_dir,wf.name,extract_WMH2.name),extract_WMH2,'output_dir')
# wf.connect(ERC2,'background_proba',extract_WMH2,'probability_image')
# wf.connect(MGDM,'segmentation',extract_WMH2,'segmentation_image')
# wf.connect(MGDM,'distance',extract_WMH2,'levelset_boundary_image')
# wf.connect(AddVentHornsirn,'out_file',extract_WMH2,'location_prior_image')
#
# extract_WMH3 = extract_WMH.clone(name='Extract_WMH3')
# extract_WMH3.inputs.gm_boundary_partial_vol_dist = 3.0
# wf.connect(subjectList,('subject_id',createOutputDir,wf.base_dir,wf.name,extract_WMH3.name),extract_WMH3,'output_dir')
# wf.connect(ERC2,'background_proba',extract_WMH3,'probability_image')
# wf.connect(MGDM,'segmentation',extract_WMH3,'segmentation_image')
# wf.connect(MGDM,'distance',extract_WMH3,'levelset_boundary_image')
# wf.connect(AddVentHornsirn,'out_file',extract_WMH3,'location_prior_image')
#===========================================================================
'''
####################################
#### FINDING SMALL WMHs ####
####################################
Small round WMHs near the cortex are often missed by the main algorithm,
so we're adding this one that takes care of them.
'''
# Recursive Ridge Diffusion : round WMH detection
round_WMH = Node(RecursiveRidgeDiffusion(), name='round_WMH')
round_WMH.plugin_args = {'sbatch_args': '--mem 6000'}
round_WMH.inputs.ridge_intensities = "bright"
round_WMH.inputs.ridge_filter = "0D"
round_WMH.inputs.orientation = "undefined"
round_WMH.inputs.ang_factor = 1.0
round_WMH.inputs.min_scale = 1
round_WMH.inputs.max_scale = 4
round_WMH.inputs.propagation_model = "none"
round_WMH.inputs.diffusion_factor = 1.0
round_WMH.inputs.similarity_scale = 0.1
round_WMH.inputs.neighborhood_size = 4
round_WMH.inputs.max_iter = 100
round_WMH.inputs.max_diff = 0.001
round_WMH.inputs.save_data = True
wf.connect(
subjectList,
('subject_id', createOutputDir, wf.base_dir, wf.name, round_WMH.name),
round_WMH, 'output_dir')
wf.connect(ERC2, 'background_proba', round_WMH, 'input_image')
wf.connect(AddVentHornsirn, 'out_file', round_WMH, 'loc_prior')
# Extract Lesions : extract round WMH
extract_round_WMH = Node(LesionExtraction(), name='Extract_round_WMH')
extract_round_WMH.plugin_args = {'sbatch_args': '--mem 6000'}
extract_round_WMH.inputs.gm_boundary_partial_vol_dist = 1.0
extract_round_WMH.inputs.csf_boundary_partial_vol_dist = 2.0
extract_round_WMH.inputs.lesion_clust_dist = 1.0
extract_round_WMH.inputs.prob_min_thresh = 0.33
extract_round_WMH.inputs.prob_max_thresh = 0.33
extract_round_WMH.inputs.small_lesion_size = 6.0
extract_round_WMH.inputs.save_data = True
extract_round_WMH.inputs.atlas_file = atlas
wf.connect(subjectList, ('subject_id', createOutputDir, wf.base_dir,
wf.name, extract_round_WMH.name),
extract_round_WMH, 'output_dir')
wf.connect(round_WMH, 'ridge_pv', extract_round_WMH, 'probability_image')
wf.connect(MGDM, 'segmentation', extract_round_WMH, 'segmentation_image')
wf.connect(MGDM, 'distance', extract_round_WMH, 'levelset_boundary_image')
wf.connect(AddVentHornsirn, 'out_file', extract_round_WMH,
'location_prior_image')
#===========================================================================
# extract_round_WMH2 = extract_round_WMH.clone(name='Extract_round_WMH2')
# extract_round_WMH2.inputs.gm_boundary_partial_vol_dist = 2.0
# wf.connect(subjectList,('subject_id',createOutputDir,wf.base_dir,wf.name,extract_round_WMH2.name),extract_round_WMH2,'output_dir')
# wf.connect(round_WMH,'ridge_pv',extract_round_WMH2,'probability_image')
# wf.connect(MGDM,'segmentation',extract_round_WMH2,'segmentation_image')
# wf.connect(MGDM,'distance',extract_round_WMH2,'levelset_boundary_image')
# wf.connect(AddVentHornsirn,'out_file',extract_round_WMH2,'location_prior_image')
#
# extract_round_WMH3 = extract_round_WMH.clone(name='Extract_round_WMH3')
# extract_round_WMH3.inputs.gm_boundary_partial_vol_dist = 2.0
# wf.connect(subjectList,('subject_id',createOutputDir,wf.base_dir,wf.name,extract_round_WMH3.name),extract_round_WMH3,'output_dir')
# wf.connect(round_WMH,'ridge_pv',extract_round_WMH3,'probability_image')
# wf.connect(MGDM,'segmentation',extract_round_WMH3,'segmentation_image')
# wf.connect(MGDM,'distance',extract_round_WMH3,'levelset_boundary_image')
# wf.connect(AddVentHornsirn,'out_file',extract_round_WMH3,'location_prior_image')
#===========================================================================
'''
####################################
#### COMBINE BOTH TYPES ####
####################################
Small round WMHs and regular WMH together before thresholding
+
PVS from white matter and internal capsule
'''
# Image calculator : WM + IC DVRS
DVRS = Node(ImageMaths(), name="DVRS")
DVRS.inputs.op_string = "-max"
DVRS.inputs.out_file = "DVRS_map.nii.gz"
wf.connect(extract_WM_pvs, 'lesion_score', DVRS, "in_file")
wf.connect(extract_IC_pvs, "lesion_score", DVRS, "in_file2")
# Image calculator : WMH + round
WMH = Node(ImageMaths(), name="WMH")
WMH.inputs.op_string = "-max"
WMH.inputs.out_file = "WMH_map.nii.gz"
wf.connect(extract_WMH, 'lesion_score', WMH, "in_file")
wf.connect(extract_round_WMH, "lesion_score", WMH, "in_file2")
#===========================================================================
# WMH2 = Node(ImageMaths(), name="WMH2")
# WMH2.inputs.op_string = "-max"
# WMH2.inputs.out_file = "WMH2_map.nii.gz"
# wf.connect(extract_WMH2,'lesion_score',WMH2,"in_file")
# wf.connect(extract_round_WMH2,"lesion_score", WMH2, "in_file2")
#
# WMH3 = Node(ImageMaths(), name="WMH3")
# WMH3.inputs.op_string = "-max"
# WMH3.inputs.out_file = "WMH3_map.nii.gz"
# wf.connect(extract_WMH3,'lesion_score',WMH3,"in_file")
# wf.connect(extract_round_WMH3,"lesion_score", WMH3, "in_file2")
#===========================================================================
# Image calculator : multiply by boundnary partial volume
WMH_mul = Node(ImageMaths(), name="WMH_mul")
WMH_mul.inputs.op_string = "-mul"
WMH_mul.inputs.out_file = "final_mask.nii.gz"
wf.connect(WMH, "out_file", WMH_mul, "in_file")
wf.connect(MGDM, "distance", WMH_mul, "in_file2")
#===========================================================================
# WMH2_mul = Node(ImageMaths(), name="WMH2_mul")
# WMH2_mul.inputs.op_string = "-mul"
# WMH2_mul.inputs.out_file = "final_mask.nii.gz"
# wf.connect(WMH2,"out_file", WMH2_mul,"in_file")
# wf.connect(MGDM,"distance", WMH2_mul, "in_file2")
#
# WMH3_mul = Node(ImageMaths(), name="WMH3_mul")
# WMH3_mul.inputs.op_string = "-mul"
# WMH3_mul.inputs.out_file = "final_mask.nii.gz"
# wf.connect(WMH3,"out_file", WMH3_mul,"in_file")
# wf.connect(MGDM,"distance", WMH3_mul, "in_file2")
#===========================================================================
'''
##########################################
#### SEGMENTATION THRESHOLD ####
##########################################
A threshold of 0.5 is very conservative, because the final lesion score is the product of two probabilities.
This needs to be optimized to a value between 0.25 and 0.5 to balance false negatives
(dominant at 0.5) and false positives (dominant at low values).
'''
# Threshold binary mask :
DVRS_mask = Node(Threshold(), name="DVRS_mask")
DVRS_mask.inputs.thresh = 0.25
DVRS_mask.inputs.direction = "below"
wf.connect(DVRS, "out_file", DVRS_mask, "in_file")
# Threshold binary mask : 025
WMH1_025 = Node(Threshold(), name="WMH1_025")
WMH1_025.inputs.thresh = 0.25
WMH1_025.inputs.direction = "below"
wf.connect(WMH_mul, "out_file", WMH1_025, "in_file")
#===========================================================================
# WMH2_025 = Node(Threshold(), name="WMH2_025")
# WMH2_025.inputs.thresh = 0.25
# WMH2_025.inputs.direction = "below"
# wf.connect(WMH2_mul,"out_file", WMH2_025, "in_file")
#
# WMH3_025 = Node(Threshold(), name="WMH3_025")
# WMH3_025.inputs.thresh = 0.25
# WMH3_025.inputs.direction = "below"
# wf.connect(WMH3_mul,"out_file", WMH3_025, "in_file")
#===========================================================================
# Threshold binary mask : 050
WMH1_050 = Node(Threshold(), name="WMH1_050")
WMH1_050.inputs.thresh = 0.50
WMH1_050.inputs.direction = "below"
wf.connect(WMH_mul, "out_file", WMH1_050, "in_file")
#===========================================================================
# WMH2_050 = Node(Threshold(), name="WMH2_050")
# WMH2_050.inputs.thresh = 0.50
# WMH2_050.inputs.direction = "below"
# wf.connect(WMH2_mul,"out_file", WMH2_050, "in_file")
#
# WMH3_050 = Node(Threshold(), name="WMH3_050")
# WMH3_050.inputs.thresh = 0.50
# WMH3_050.inputs.direction = "below"
# wf.connect(WMH3_mul,"out_file", WMH3_050, "in_file")
#===========================================================================
# Threshold binary mask : 075
WMH1_075 = Node(Threshold(), name="WMH1_075")
WMH1_075.inputs.thresh = 0.75
WMH1_075.inputs.direction = "below"
wf.connect(WMH_mul, "out_file", WMH1_075, "in_file")
#===========================================================================
# WMH2_075 = Node(Threshold(), name="WMH2_075")
# WMH2_075.inputs.thresh = 0.75
# WMH2_075.inputs.direction = "below"
# wf.connect(WMH2_mul,"out_file", WMH2_075, "in_file")
#
# WMH3_075 = Node(Threshold(), name="WMH3_075")
# WMH3_075.inputs.thresh = 0.75
# WMH3_075.inputs.direction = "below"
# wf.connect(WMH3_mul,"out_file", WMH3_075, "in_file")
#===========================================================================
## Outputs
DVRS_Output = Node(IdentityInterface(fields=[
'mask', 'region', 'lesion_size', 'lesion_proba', 'boundary', 'label',
'score'
]),
name='DVRS_Output')
wf.connect(DVRS_mask, 'out_file', DVRS_Output, 'mask')
WMH_output = Node(IdentityInterface(fields=[
'mask1025', 'mask1050', 'mask1075', 'mask2025', 'mask2050', 'mask2075',
'mask3025', 'mask3050', 'mask3075'
]),
name='WMH_output')
wf.connect(WMH1_025, 'out_file', WMH_output, 'mask1025')
#wf.connect(WMH2_025,'out_file',WMH_output,'mask2025')
#wf.connect(WMH3_025,'out_file',WMH_output,'mask3025')
wf.connect(WMH1_050, 'out_file', WMH_output, 'mask1050')
#wf.connect(WMH2_050,'out_file',WMH_output,'mask2050')
#wf.connect(WMH3_050,'out_file',WMH_output,'mask3050')
wf.connect(WMH1_075, 'out_file', WMH_output, 'mask1075')
#wf.connect(WMH2_075,'out_file',WMH_output,'mask2070')
#wf.connect(WMH3_075,'out_file',WMH_output,'mask3075')
return wf
# FSL randomise for higher level analysis
highermodel = Node(Randomise(tfce=True,
raw_stats_imgs=True,
design_mat=group_mat,
tcon=group_con),
name='highermodel')
## Cluster results
# make binary masks of sig clusters
binarize = Node(Binarize(min=0.95, max=1.0),
name='binarize',
iterfield='in_file')
# mask T-map before clustering
mask_tmaps = Node(ApplyMask(), name='mask_tmaps')
# clusterize and extract cluster stats/peaks
clusterize = Node(Cluster(threshold=2.3,
out_index_file='outindex.nii',
out_localmax_txt_file='localmax.txt'),
name='clusterize')
# make pictures if time
# In[ ]:
sbc2_workflow2 = Workflow(name='sbc2_workflow2')
sbc2_workflow2.connect([(infosource2, datagrabber, [('roi', 'roi')]),
(datagrabber, merge, [('roi', 'in_files')]),
(merge, highermodel, [('merged_file', 'in_file')]),
def _run_interface(self, runtime):
# Loading required packages
from BrainTypes_additional_interfaces import DipyDenoise
from nipype.interfaces.diffusion_toolkit.dti import DTIRecon
import nipype.interfaces.fsl as fsl
import nipype.pipeline.engine as pe
import nipype.interfaces.io as nio
import nipype.interfaces.utility as util
from nipype.algorithms.misc import Gunzip
from nipype.interfaces.fsl.maths import ApplyMask
import os
# ==============================================================
# Processing of diffusion-weighted data
# Extract b0 image
fslroi = pe.Node(interface=fsl.ExtractROI(), name='extract_b0')
fslroi.inputs.in_file = self.inputs.dwi
fslroi.inputs.t_min = 0
fslroi.inputs.t_size = 1
# Create a brain mask
bet = pe.Node(interface=fsl.BET(frac=0.3,
robust=False,
mask=True,
no_output=False),
name='bet')
# Eddy-current and motion correction
eddy = pe.Node(interface=fsl.epi.Eddy(args='-v'), name='eddy')
eddy.inputs.in_acqp = self.inputs.acqparams
eddy.inputs.in_bvec = self.inputs.bvecs
eddy.inputs.in_bval = self.inputs.bvals
eddy.inputs.in_file = self.inputs.dwi
eddy.inputs.in_index = self.inputs.index_file
# Denoising
dwi_denoise = pe.Node(interface=DipyDenoise(), name='dwi_denoise')
dwi_denoise.inputs.in_file = self.inputs.dwi
# Fitting the diffusion tensor model
dtifit = pe.Node(interface=DTIRecon(), name='dtifit')
dtifit.inputs.out_prefix = self.inputs.subject_id
dtifit.inputs.bvals = self.inputs.bvals
dtifit.inputs.bvecs = self.inputs.bvecs
# Applying the mask
FA_applymask = pe.Node(interface=ApplyMask(), name='FA_applymask')
# Renaming the outputs
b0_rename = pe.Node(interface=util.Rename(keep_ext=True),
name='b0_rename')
b0_rename.inputs.format_string = self.inputs.subject_id + '_b0'
colourFA_rename = pe.Node(interface=util.Rename(keep_ext=True),
name='colourFA_rename')
colourFA_rename.inputs.format_string = self.inputs.subject_id + '_colourFA'
FA_rename = pe.Node(interface=util.Rename(keep_ext=True),
name='FA_rename')
FA_rename.inputs.format_string = self.inputs.subject_id + '_FA'
dwi_rename = pe.Node(interface=util.Rename(keep_ext=True),
name='dwi_rename')
dwi_rename.inputs.format_string = self.inputs.subject_id + '_dwi'
mask_rename = pe.Node(interface=util.Rename(keep_ext=True),
name='mask_rename')
mask_rename.inputs.format_string = self.inputs.subject_id + '_mask'
# Collect everything
datasink = pe.Node(nio.DataSink(), name='sinker')
datasink.inputs.parameterization = False
datasink.inputs.base_directory = self.inputs.out_directory + '/_subject_id_' + self.inputs.subject_id
# ==============================================================
# Setting up the workflow
dwi_preproc = pe.Workflow(name='dwi_preproc')
# Diffusion data
# Preprocessing
dwi_preproc.connect(fslroi, 'roi_file', bet, 'in_file')
dwi_preproc.connect(bet, 'mask_file', eddy, 'in_mask')
dwi_preproc.connect(eddy, 'out_corrected', dwi_denoise, 'in_file')
# Calculate diffusion measures
dwi_preproc.connect(dwi_denoise, 'out_file', dtifit, 'DWI')
# Applying the mask to the scalar images
dwi_preproc.connect(dtifit, 'FA', FA_applymask, 'in_file')
dwi_preproc.connect(bet, 'mask_file', FA_applymask, 'mask_file')
# Renaming
dwi_preproc.connect(fslroi, 'roi_file', b0_rename, 'in_file')
dwi_preproc.connect(dwi_denoise, 'out_file', dwi_rename, 'in_file')
dwi_preproc.connect(FA_applymask, 'out_file', FA_rename, 'in_file')
dwi_preproc.connect(dtifit, 'FA_color', colourFA_rename, 'in_file')
dwi_preproc.connect(bet, 'mask_file', mask_rename, 'in_file')
# Moving the results to the datasink
dwi_preproc.connect(b0_rename, 'out_file', datasink,
'preprocessed.@b0')
dwi_preproc.connect(colourFA_rename, 'out_file', datasink,
'preprocessed.@colourFA')
dwi_preproc.connect(dwi_rename, 'out_file', datasink,
'preprocessed.@dwi')
dwi_preproc.connect(FA_rename, 'out_file', datasink,
'preprocessed.@FA')
dwi_preproc.connect(mask_rename, 'out_file', datasink,
'preprocessed.@mask')
# ==============================================================
# Running the workflow
dwi_preproc.base_dir = os.path.abspath(self.inputs.out_directory +
'_subject_id_' +
self.inputs.subject_id)
dwi_preproc.write_graph()
dwi_preproc.run()
return runtime
# Registration- using FLIRT
# The BOLD image is 'in_file', the anat is 'reference', the output is 'out_file'
coreg1 = Node(FLIRT(), name='coreg1')
coreg2 = Node(FLIRT(apply_xfm=True), name='coreg2')
# make binary mask
# structural is the 'in_file', output is 'binary_file'
binarize_struct = Node(Binarize(dilate=mask_dilation,
erode=mask_erosion,
min=1),
name='binarize_struct')
# apply the binary mask to the functional data
# functional is 'in_file', binary mask is 'mask_file', output is 'out_file'
mask_func = Node(ApplyMask(), name='mask_func')
# Artifact detection for scrubbing/motion assessment
art = Node(
ArtifactDetect(
mask_type='file',
parameter_source='FSL',
norm_threshold=
0.5, #mutually exclusive with rotation and translation thresh
zintensity_threshold=3,
use_differences=[True, False]),
name='art')
def converthex_xform(orig_xform):
from numpy import genfromtxt, savetxt
def longitudinal_registration(sub_id,
datasource,
sessions,
reference,
result_dir,
nipype_cache,
bet_workflow=None):
"""
This is a workflow to register multi-modalities MR (T2, T1KM, FLAIR) to their
reference T1 image, in multiple time-points cohort. In particular, for each
subject, this workflow will register the MR images in each time-point (tp)
to the corresponding T1, then it will register all the T1 images to a reference T1
(the one that is the closest in time to the radiotherapy session), and finally the
reference T1 to the BPLCT. At the end, all the MR images will be saved both in T1 space
(for each tp) and in CT space.
"""
reg2T1 = nipype.MapNode(interface=AntsRegSyn(),
iterfield=['input_file'],
name='reg2T1')
reg2T1.inputs.transformation = 's'
reg2T1.inputs.num_dimensions = 3
reg2T1.inputs.num_threads = 6
if reference:
regT12CT = nipype.MapNode(interface=AntsRegSyn(),
iterfield=['input_file'],
name='regT12CT')
regT12CT.inputs.transformation = 'r'
regT12CT.inputs.num_dimensions = 3
regT12CT.inputs.num_threads = 4
reg_nodes = []
for i in range(3):
reg = nipype.MapNode(interface=AntsRegSyn(),
iterfield=['input_file', 'ref_file'],
name='ants_reg{}'.format(i))
reg.inputs.transformation = 'r'
reg.inputs.num_dimensions = 3
reg.inputs.num_threads = 4
reg.inputs.interpolation = 'BSpline'
reg_nodes.append(reg)
apply_mask_nodes = []
for i in range(3):
masking = nipype.MapNode(interface=ApplyMask(),
iterfield=['in_file', 'mask_file'],
name='masking{}'.format(i))
apply_mask_nodes.append(masking)
apply_ts_nodes = []
for i in range(3):
apply_ts = nipype.MapNode(interface=ApplyTransforms(),
iterfield=['input_image', 'transforms'],
name='apply_ts{}'.format(i))
apply_ts_nodes.append(apply_ts)
# Apply ts nodes for T1_ref normalization
apply_ts_nodes1 = []
for i in range(3):
apply_ts = nipype.MapNode(interface=ApplyTransforms(),
iterfield=['input_image', 'transforms'],
name='apply_ts1{}'.format(i))
apply_ts_nodes1.append(apply_ts)
split_ds_nodes = []
for i in range(4):
split_ds = nipype.Node(interface=Split(), name='split_ds{}'.format(i))
split_ds.inputs.splits = [1] * len(sessions)
split_ds_nodes.append(split_ds)
apply_ts_t1 = nipype.MapNode(interface=ApplyTransforms(),
iterfield=['input_image', 'transforms'],
name='apply_ts_t1')
merge_nodes = []
if reference:
iterfields = ['in1', 'in2', 'in3', 'in4']
iterfields_t1 = ['in1', 'in2', 'in3']
if_0 = 2
else:
iterfields = ['in1', 'in2', 'in3']
iterfields_t1 = ['in1', 'in2']
if_0 = 1
for i in range(3):
merge = nipype.MapNode(interface=Merge(len(iterfields)),
iterfield=iterfields,
name='merge{}'.format(i))
merge.inputs.ravel_inputs = True
merge_nodes.append(merge)
# Merging transforms for normalization to T1_ref
merge_nodes1 = []
for i in range(3):
merge = nipype.MapNode(interface=Merge(3),
iterfield=['in1', 'in2', 'in3'],
name='merge1{}'.format(i))
merge.inputs.ravel_inputs = True
merge_nodes1.append(merge)
merge_ts_t1 = nipype.MapNode(interface=Merge(len(iterfields_t1)),
iterfield=iterfields_t1,
name='merge_t1')
merge_ts_t1.inputs.ravel_inputs = True
# have to create a fake merge of the transformation from t10 to CT in order
# to have the same number if matrices as input in mapnode
fake_merge = nipype.Node(interface=Merge(len(sessions)), name='fake_merge')
datasink = nipype.Node(nipype.DataSink(base_directory=result_dir),
"datasink")
substitutions = [('subid', sub_id)]
for i, session in enumerate(sessions):
substitutions += [('session'.format(i), session)]
substitutions += [('_masking0{}/antsregWarped_masked.nii.gz'.format(i),
session + '/' + 'CT1_preproc.nii.gz')]
substitutions += [('_reg2T1{}/antsreg0GenericAffine.mat'.format(i),
session + '/' + 'reg2T1_ref.mat')]
substitutions += [('_reg2T1{}/antsreg1Warp.nii.gz'.format(i),
session + '/' + 'reg2T1_ref_warp.nii.gz')]
substitutions += [('_reg2T1{}/antsregWarped.nii.gz'.format(i),
session + '/' + 'T1_reg2T1_ref.nii.gz')]
substitutions += [('_regT12CT{}/antsreg0GenericAffine.mat'.format(i),
'/regT1_ref2CT.mat')]
substitutions += [('_masking1{}/antsregWarped_masked.nii.gz'.format(i),
session + '/' + 'T2_preproc.nii.gz')]
substitutions += [('_masking2{}/antsregWarped_masked.nii.gz'.format(i),
session + '/' + 'FLAIR_preproc.nii.gz')]
substitutions += [('_apply_ts0{}/CT1_trans.nii.gz'.format(i),
session + '/' + 'CT1_reg2CT.nii.gz')]
substitutions += [('_apply_ts1{}/T2_trans.nii.gz'.format(i),
session + '/' + 'T2_reg2CT.nii.gz')]
substitutions += [('_apply_ts2{}/FLAIR_trans.nii.gz'.format(i),
session + '/' + 'FLAIR_reg2CT.nii.gz')]
substitutions += [('_apply_ts_t1{}/T1_trans.nii.gz'.format(i),
session + '/' + 'T1_reg2CT.nii.gz')]
substitutions += [('_apply_ts10{}/CT1_trans.nii.gz'.format(i),
session + '/' + 'CT1_reg2T1_ref.nii.gz')]
substitutions += [('_apply_ts11{}/T2_trans.nii.gz'.format(i),
session + '/' + 'T2_reg2T1_ref.nii.gz')]
substitutions += [('_apply_ts12{}/FLAIR_trans.nii.gz'.format(i),
session + '/' + 'FLAIR_reg2T1_ref.nii.gz')]
datasink.inputs.substitutions = substitutions
# Create Workflow
workflow = nipype.Workflow('registration_workflow', base_dir=nipype_cache)
for i, reg in enumerate(reg_nodes):
workflow.connect(datasource, SEQUENCES[i + 1], reg, 'input_file')
workflow.connect(datasource, SEQUENCES[0], reg, 'ref_file')
# bring every MR in CT space
for i, node in enumerate(apply_ts_nodes):
workflow.connect(datasource, SEQUENCES[i + 1], node, 'input_image')
if reference:
workflow.connect(datasource, 'reference', node, 'reference_image')
else:
workflow.connect(datasource, 't1_0', node, 'reference_image')
workflow.connect(merge_nodes[i], 'out', node, 'transforms')
workflow.connect(node, 'output_image', datasink,
'results.subid.@{}_reg2CT'.format(SEQUENCES[i + 1]))
# bring every MR in T1_ref space
for i, node in enumerate(apply_ts_nodes1):
workflow.connect(datasource, SEQUENCES[i + 1], node, 'input_image')
workflow.connect(datasource, 't1_0', node, 'reference_image')
workflow.connect(merge_nodes1[i], 'out', node, 'transforms')
workflow.connect(
node, 'output_image', datasink,
'results.subid.@{}_reg2T1_ref'.format(SEQUENCES[i + 1]))
for i, node in enumerate(merge_nodes):
workflow.connect(reg_nodes[i], 'regmat', node, 'in{}'.format(if_0 + 2))
workflow.connect(reg2T1, 'regmat', node, 'in{}'.format(if_0 + 1))
workflow.connect(reg2T1, 'warp_file', node, 'in{}'.format(if_0))
if reference:
workflow.connect(fake_merge, 'out', node, 'in1')
for i, node in enumerate(merge_nodes1):
workflow.connect(reg_nodes[i], 'regmat', node, 'in3')
workflow.connect(reg2T1, 'regmat', node, 'in2')
workflow.connect(reg2T1, 'warp_file', node, 'in1')
for i, mask in enumerate(apply_mask_nodes):
workflow.connect(reg_nodes[i], 'reg_file', mask, 'in_file')
if bet_workflow is not None:
workflow.connect(bet_workflow, 'bet.out_mask', mask, 'mask_file')
else:
workflow.connect(datasource, 't1_mask', mask, 'mask_file')
workflow.connect(mask, 'out_file', datasink,
'results.subid.@{}_preproc'.format(SEQUENCES[i + 1]))
if bet_workflow is not None:
workflow.connect(bet_workflow, 'bet.out_file', reg2T1, 'input_file')
workflow.connect(bet_workflow, 't1_0_bet.out_file', reg2T1, 'ref_file')
else:
workflow.connect(datasource, 't1_bet', reg2T1, 'input_file')
workflow.connect(datasource, 't1_0_bet', reg2T1, 'ref_file')
if reference:
for i, sess in enumerate(sessions):
workflow.connect(regT12CT, 'regmat', fake_merge,
'in{}'.format(i + 1))
workflow.connect(regT12CT, 'regmat', datasink,
'results.subid.{0}.@regT12CT_mat'.format(sess))
workflow.connect(datasource, 'reference', regT12CT, 'ref_file')
workflow.connect(datasource, 't1_0', regT12CT, 'input_file')
workflow.connect(fake_merge, 'out', merge_ts_t1, 'in1')
workflow.connect(datasource, 'reference', apply_ts_t1,
'reference_image')
else:
workflow.connect(datasource, 't1_0', apply_ts_t1, 'reference_image')
workflow.connect(datasource, 't1', apply_ts_t1, 'input_image')
workflow.connect(merge_ts_t1, 'out', apply_ts_t1, 'transforms')
workflow.connect(reg2T1, 'regmat', merge_ts_t1, 'in{}'.format(if_0 + 1))
workflow.connect(reg2T1, 'warp_file', merge_ts_t1, 'in{}'.format(if_0))
workflow.connect(reg2T1, 'warp_file', datasink,
'results.subid.@reg2CT_warp')
workflow.connect(reg2T1, 'regmat', datasink, 'results.subid.@reg2CT_mat')
workflow.connect(reg2T1, 'reg_file', datasink, 'results.subid.@T12T1_ref')
workflow.connect(apply_ts_t1, 'output_image', datasink,
'results.subid.@T1_reg2CT')
if bet_workflow is not None:
workflow = datasink_base(datasink, datasource, workflow, sessions,
reference)
else:
workflow = datasink_base(datasink,
datasource,
workflow,
sessions,
reference,
extra_nodes=['t1_bet'])
return workflow
def single_tp_registration(sub_id,
datasource,
session,
reference,
result_dir,
nipype_cache,
bet_workflow=None):
"""
This is a workflow to register multi-modalities MR (T2, T1KM, FLAIR) to their
reference T1 image, in one single time-point cohort. In particular, for each
subject, this workflow will register the MR images in the provided time-point (tp)
to the corresponding T1, then it will register the T1 image to the BPLCT (if present)'
'. At the end, all the MR images will be saved both in T1 space and in CT space.
"""
session = session[0]
if reference:
regT12CT = nipype.MapNode(interface=AntsRegSyn(),
iterfield=['input_file'],
name='regT12CT')
regT12CT.inputs.transformation = 'r'
regT12CT.inputs.num_dimensions = 3
regT12CT.inputs.num_threads = 4
reg_nodes = []
for i in range(3):
reg = nipype.MapNode(interface=AntsRegSyn(),
iterfield=['input_file', 'ref_file'],
name='ants_reg{}'.format(i))
reg.inputs.transformation = 'r'
reg.inputs.num_dimensions = 3
reg.inputs.num_threads = 4
reg.inputs.interpolation = 'BSpline'
reg_nodes.append(reg)
apply_mask_nodes = []
for i in range(3):
masking = nipype.MapNode(interface=ApplyMask(),
iterfield=['in_file', 'mask_file'],
name='masking{}'.format(i))
apply_mask_nodes.append(masking)
if reference:
apply_ts_nodes = []
for i in range(3):
apply_ts = nipype.MapNode(interface=ApplyTransforms(),
iterfield=['input_image', 'transforms'],
name='apply_ts{}'.format(i))
apply_ts_nodes.append(apply_ts)
apply_ts_t1 = nipype.MapNode(interface=ApplyTransforms(),
iterfield=['input_image', 'transforms'],
name='apply_ts_t1')
merge_nodes = []
for i in range(3):
merge = nipype.MapNode(interface=Merge(2),
iterfield=['in1', 'in2'],
name='merge{}'.format(i))
merge.inputs.ravel_inputs = True
merge_nodes.append(merge)
datasink = nipype.Node(nipype.DataSink(base_directory=result_dir),
"datasink")
substitutions = [('subid', sub_id)]
substitutions += [('session', session)]
substitutions += [('_regT12CT0/antsreg0GenericAffine.mat',
'/reg2T1_ref.mat')]
substitutions += [('_masking00/antsregWarped_masked.nii.gz',
session + '/' + 'CT1_preproc.nii.gz')]
substitutions += [('_regT12CT/antsreg0GenericAffine.mat',
'/regT1_ref2CT.mat')]
substitutions += [('_masking10/antsregWarped_masked.nii.gz',
session + '/' + 'T2_preproc.nii.gz')]
substitutions += [('_masking20/antsregWarped_masked.nii.gz',
session + '/' + 'FLAIR_preproc.nii.gz')]
substitutions += [('_apply_ts00/antsregWarped_masked_trans.nii.gz',
session + '/' + 'CT1_reg2CT.nii.gz')]
substitutions += [('_apply_ts10/antsregWarped_masked_trans.nii.gz',
session + '/' + 'T2_reg2CT.nii.gz')]
substitutions += [('_apply_ts20/antsregWarped_masked_trans.nii.gz',
session + '/' + 'FLAIR_reg2CT.nii.gz')]
substitutions += [('_apply_ts_t10/T1_preproc_trans.nii.gz',
session + '/' + 'T1_reg2CT.nii.gz')]
datasink.inputs.substitutions = substitutions
# Create Workflow
workflow = nipype.Workflow('registration_workflow', base_dir=nipype_cache)
for i, reg in enumerate(reg_nodes):
workflow.connect(datasource, SEQUENCES[i + 1], reg, 'input_file')
workflow.connect(datasource, SEQUENCES[0], reg, 'ref_file')
# bring every MR in CT space
if reference:
for i, node in enumerate(merge_nodes):
workflow.connect(reg_nodes[i], 'regmat', node, 'in2')
workflow.connect(regT12CT, 'regmat', node, 'in1')
for i, node in enumerate(apply_ts_nodes):
workflow.connect(apply_mask_nodes[i], 'out_file', node,
'input_image')
workflow.connect(datasource, 'reference', node, 'reference_image')
workflow.connect(regT12CT, 'regmat', node, 'transforms')
workflow.connect(
node, 'output_image', datasink,
'results.subid.@{}_reg2CT'.format(SEQUENCES[i + 1]))
workflow.connect(regT12CT, 'regmat', datasink,
'results.subid.{0}.@regT12CT_mat'.format(session))
workflow.connect(datasource, 'reference', regT12CT, 'ref_file')
workflow.connect(datasource, 't1', regT12CT, 'input_file')
if bet_workflow is not None:
workflow.connect(bet_workflow, 'bet.out_file', apply_ts_t1,
'input_image')
else:
workflow.connect(datasource, 't1_bet', apply_ts_t1, 'input_image')
workflow.connect(datasource, 'reference', apply_ts_t1,
'reference_image')
workflow.connect(apply_ts_t1, 'output_image', datasink,
'results.subid.@T1_reg2CT')
workflow.connect(regT12CT, 'regmat', apply_ts_t1, 'transforms')
for i, mask in enumerate(apply_mask_nodes):
workflow.connect(reg_nodes[i], 'reg_file', mask, 'in_file')
if bet_workflow is not None:
workflow.connect(bet_workflow, 'bet.out_mask', mask, 'mask_file')
else:
workflow.connect(datasource, 't1_mask', mask, 'mask_file')
workflow.connect(mask, 'out_file', datasink,
'results.subid.@{}_preproc'.format(SEQUENCES[i + 1]))
if bet_workflow is not None:
workflow = datasink_base(datasink,
datasource,
workflow, [session],
reference,
t10=False)
else:
workflow = datasink_base(datasink,
datasource,
workflow, [session],
reference,
extra_nodes=['t1_bet'],
t10=False)
return workflow
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 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
string_list = [c2, c3]
return (first_tissue, string_list)
pre_merge = Node(Function(input_names=['c1', 'c2', 'c3'],
output_names=['first_tissue', 'string_list'],
function=extract_tissue_c123),
name='Pre_Merge_Tissues')
merge_tissues = Node(MultiImageMaths(), name="Merge_C1_C2_C3")
merge_tissues.inputs.op_string = "-add %s -add %s -thr 0.05 -bin"
fill_mask = Node(UnaryMaths(), name="FillHoles_Mask")
fill_mask.inputs.operation = "fillh"
apply_mask_t1 = Node(ApplyMask(), name="ApplyMask_T1")
apply_mask_flair = Node(ApplyMask(), name="ApplyMask_FLAIR")
apply_mask_swi = Node(ApplyMask(), name="ApplyMask_SWI")
apply_mask_bct1 = Node(ApplyMask(), name="ApplyMask_BiasCorrect_T1")
###SNR
#Tissue 1-3 mask construction and HeadMask construction.
con_tissue_mask_1 = Node(Threshold(), name="Tissue1_Mask")
con_tissue_mask_1.inputs.thresh = 0.1
con_tissue_mask_1.inputs.args = "-bin"
con_tissue_mask_2 = Node(Threshold(), name="Tissue2_Mask")
con_tissue_mask_2.inputs.thresh = 0.1
con_tissue_mask_2.inputs.args = "-bin"
con_tissue_mask_3 = Node(Threshold(), name="Tissue3_Mask")
con_tissue_mask_3.inputs.thresh = 0.1
con_tissue_mask_3.inputs.args = "-bin"
def init_brain_extraction_wf(tpl_target_path,
tpl_mask_path,
tpl_regmask_path,
name='brain_extraction_wf',
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):
"""
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::
:graph2use: orig
:simple_form: yes
from niworkflows.anat 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 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
(optional) Mask used for registration to limit the metric
computation to a specific region.
**Outputs**
out_file
Skull-stripped and :abbr:`INU (intensity non-uniformity)`-corrected ``in_files``
out_mask
Calculated brain mask
bias_corrected
The ``in_files`` input images, after :abbr:`INU (intensity non-uniformity)`
correction, before skull-stripping.
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
"""
# 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)
# # Get probabilistic brain mask if available
inputnode = pe.Node(niu.IdentityInterface(fields=['in_files', 'in_mask']),
name='inputnode')
# # Try to find a registration mask, set if available
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,
mem_gb=2.5)
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('CPAC.anat_preproc', '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'])
# Apply mask
apply_mask = pe.MapNode(ApplyMask(),
iterfield=['in_file'],
name='apply_mask')
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')]),
])
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')
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')]),
])
else:
norm.inputs.fixed_image = tpl_target_path
wf.connect([
(inu_n4, norm, [(('output_image', _pop), 'moving_image')]),
])
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)
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')]),
])
return wf
regT12CT.inputs.num_dimensions = 3
regT12CT.inputs.num_threads = 4
reg_nodes = []
for i in range(3):
reg = nipype.MapNode(interface=AntsRegSyn(), iterfield=['input_file', 'ref_file'],
name='ants_reg{}'.format(i))
reg.inputs.transformation = 'r'
reg.inputs.num_dimensions = 3
reg.inputs.num_threads = 4
reg.inputs.interpolation = 'BSpline'
reg_nodes.append(reg)
apply_mask_nodes = []
for i in range(3):
masking = nipype.MapNode(interface=ApplyMask(), iterfield=['in_file', 'mask_file'],
name='masking{}'.format(i))
apply_mask_nodes.append(masking)
apply_ts_nodes = []
for i in range(3):
apply_ts = nipype.MapNode(interface=ApplyTransforms(),
iterfield=['input_image', 'transforms'],
name='apply_ts{}'.format(i))
apply_ts_nodes.append(apply_ts)
merge_nodes = []
for i in range(3):
merge = nipype.MapNode(interface=Merge(4),
iterfield=['in1', 'in2', 'in3', 'in4'],
name='merge{}'.format(i))
