def test_Reorient2Std_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,
),
out_file=dict(argstr='%s',
genfile=True,
hash_files=False,
),
output_type=dict(),
terminal_output=dict(mandatory=True,
nohash=True,
),
)
inputs = Reorient2Std.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_Reorient2Std_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,
),
out_file=dict(argstr='%s',
genfile=True,
hash_files=False,
),
output_type=dict(),
terminal_output=dict(nohash=True,
),
)
inputs = Reorient2Std.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_Reorient2Std_outputs():
output_map = dict(out_file=dict(),
)
outputs = Reorient2Std.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_Reorient2Std_outputs():
output_map = dict(out_file=dict(),
)
outputs = Reorient2Std.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 workflow(self):
# self.datasource()
datasource = self.data_source
dict_sequences = self.dict_sequences
nipype_cache = self.nipype_cache
result_dir = self.result_dir
sub_id = self.sub_id
tobet = {**dict_sequences['MR-RT'], **dict_sequences['OT']}
workflow = nipype.Workflow('brain_extraction_workflow',
base_dir=nipype_cache)
datasink = nipype.Node(nipype.DataSink(base_directory=result_dir),
"datasink")
substitutions = [('subid', sub_id)]
substitutions += [('results/', '{}/'.format(self.workflow_name))]
substitutions += [('_preproc_corrected.', '_preproc.')]
datasink.inputs.substitutions = substitutions
for key in tobet:
files = []
# if tobet[key]['ref'] is not None:
# files.append(tobet[key]['ref'])
if tobet[key]['scans'] is not None:
files = files + tobet[key]['scans']
for el in files:
el = el.strip(self.extention)
node_name = '{0}_{1}'.format(key, el)
bet = nipype.Node(interface=HDBet(),
name='{}_bet'.format(node_name),
serial=True)
bet.inputs.save_mask = 1
bet.inputs.out_file = '{}_preproc'.format(el)
reorient = nipype.Node(interface=Reorient2Std(),
name='{}_reorient'.format(node_name))
if el in TON4:
n4 = nipype.Node(interface=N4BiasFieldCorrection(),
name='{}_n4'.format(node_name))
workflow.connect(bet, 'out_file', n4, 'input_image')
workflow.connect(bet, 'out_mask', n4, 'mask_image')
workflow.connect(
n4, 'output_image', datasink,
'results.subid.{0}.@{1}_preproc'.format(key, el))
else:
workflow.connect(
bet, 'out_file', datasink,
'results.subid.{0}.@{1}_preproc'.format(key, el))
workflow.connect(
bet, 'out_mask', datasink,
'results.subid.{0}.@{1}_preproc_mask'.format(key, el))
workflow.connect(reorient, 'out_file', bet, 'input_file')
workflow.connect(datasource, node_name, reorient, 'in_file')
return workflow
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
return(sample_template)
# In[4]:
######### Template creation nodes #########
#convert freesurfer brainmask files to .nii
convertT1 = MapNode(MRIConvert(out_file='T1.nii.gz',
out_type='niigz',
out_orientation='RAS'),
name='convertT1',
iterfield = ['in_file'])
#reorient files to standard space
reorientT1 = MapNode(Reorient2Std(out_file = 'brain_reorient.nii.gz'),
name = 'reorientT1',
iterfield = ['in_file'])
#pass files into template function (normalized, pre-skull-stripping)
makeTemplate = Node(Function(input_names=['subject_T1s','num_proc','output_prefix'],
output_names=['sample_template'],
function=make3DTemplate),
name='makeTemplate')
makeTemplate.inputs.num_proc=16 # feel free to change to suit what's free on SNI=VCS
makeTemplate.inputs.output_prefix='ELS_CT_'
# In[5]:
fssource = Node(FreeSurferSource(subjects_dir=fs_dir),
run_without_submitting=True,
name='fssource')
# Datasink- where our select outputs will go
substitutions = [('_subject_id_', '')] #output file name substitutions
datasink = Node(DataSink(substitutions=substitutions), name='datasink')
datasink.inputs.base_directory = output_dir
datasink.inputs.container = output_dir
# In[3]:
## Nodes for preprocessing
# Reorient to standard space using FSL
reorientfunc = Node(Reorient2Std(), name='reorientfunc')
reorientstruct = Node(Reorient2Std(), name='reorientstruct')
# Reslice- using MRI_convert
reslice = Node(MRIConvert(vox_size=resampled_voxel_size, out_type='nii'),
name='reslice')
# Segment structural scan
#segment = Node(Segment(affine_regularization='none'), name='segment')
segment = Node(FAST(no_bias=True, segments=True, number_classes=3),
name='segment')
#Slice timing correction based on interleaved acquisition using FSL
slicetime_correct = Node(SliceTimer(interleaved=interleave,
slice_direction=slice_dir,
time_repetition=TR),
return(sample_template)
# In[ ]:
######### Template creation nodes #########
#convert freesurfer brainmask files to .nii
convertT1 = MapNode(MRIConvert(out_file='brainmask.nii.gz',
out_type='niigz'),
name='convertT1',
iterfield = ['in_file'])
#reorient files to standard space
reorientT1 = MapNode(Reorient2Std(),
name = 'reorientT1',
iterfield = ['in_file'])
#pass files into template function (normalized, pre-skull-stripping)
makeTemplate = Node(Function(input_names=['subject_T1s','num_proc','output_prefix'],
output_names=['sample_template'],
function=make3DTemplate),
name='makeTemplate')
makeTemplate.inputs.num_proc=8 # feel free to change to suit what's free on SNI-VCS
makeTemplate.inputs.output_prefix='ELS_CT_'
# In[ ]:
sample_template = abspath(output_prefix + 'template0.nii.gz')
return (sample_template)
# In[ ]:
######### Template creation nodes #########
#convert freesurfer brainmask files to .nii
convertT1 = MapNode(MRIConvert(out_file='brainmask.nii.gz', out_type='niigz'),
name='convertT1',
iterfield=['in_file'])
#reorient files to standard space
reorientT1 = MapNode(Reorient2Std(), name='reorientT1', iterfield=['in_file'])
#pass files into template function (normalized, pre-skull-stripping)
makeTemplate = Node(Function(
input_names=['subject_T1s', 'num_proc', 'output_prefix'],
output_names=['sample_template'],
function=make3DTemplate),
name='makeTemplate')
makeTemplate.inputs.num_proc = 8 # feel free to change to suit what's free on SNI-VCS
makeTemplate.inputs.output_prefix = 'ELS_CT_'
# ## Template Subject Tissue Segmentation Workflow
# These cells are associated with the template subjects tissue segmentation workflow. The point is ultimately create 5 tissue class per subject: cerebrospinal fluid, cortical gray matter, subcortical gray matter, white matter, and whole brain.
# * Custom functions
# * Nodes
# * Workflow
#: Requires the following input by user:
#: dcm_prov_node.inputs.subtype = 'T1W'
dcm_prov_node = pe.Node(Function(input_names=['subj_obj',
'visit',
'subtype'],
output_names=['dcm_list'],
function=dcm_provider),
name='dcm_prov_node')
#: MapNode (i.e., takes list as an input type) for Freesurfer's MRIConvert
dcm_2_nii = pe.MapNode(MRIConvert(), name='dcm_2_nii', iterfield=['in_file'])
#: MapNode (i.e., takes list as an input type) for FSL's reorient2std
reorient = pe.MapNode(Reorient2Std(), name='reorient', iterfield=['in_file'])
#: Conversion WorkFlow that connects MRIConvert to reorient2std
conversion_wf = pe.Workflow(name='conversion_wf')
conversion_wf.connect([(dcm_prov_node, dcm_2_nii, [('dcm_list', 'in_file')]),
(dcm_2_nii, reorient, [('out_file', 'in_file')]),
])
'''
Specialized DTI conversion node and worflow
'''
#: MapNode (i.e., takes list as an input type) for Freesurfer's MRIConvert
dti_dcm_2_nii = pe.MapNode(DTIMRIConvert(),
name='dti_dcm_2_nii',
iterfield=['in_file'])
