def test_BRAINSROIAuto_outputs():
output_map = dict(
outputROIMaskVolume=dict(),
outputVolume=dict(),
)
outputs = BRAINSROIAuto.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_BRAINSROIAuto_inputs():
input_map = dict(
ROIAutoDilateSize=dict(argstr='--ROIAutoDilateSize %f', ),
args=dict(argstr='%s', ),
closingSize=dict(argstr='--closingSize %f', ),
cropOutput=dict(argstr='--cropOutput ', ),
environ=dict(
nohash=True,
usedefault=True,
),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
inputVolume=dict(argstr='--inputVolume %s', ),
maskOutput=dict(argstr='--maskOutput ', ),
numberOfThreads=dict(argstr='--numberOfThreads %d', ),
otsuPercentileThreshold=dict(argstr='--otsuPercentileThreshold %f', ),
outputROIMaskVolume=dict(
argstr='--outputROIMaskVolume %s',
hash_files=False,
),
outputVolume=dict(
argstr='--outputVolume %s',
hash_files=False,
),
outputVolumePixelType=dict(argstr='--outputVolumePixelType %s', ),
terminal_output=dict(nohash=True, ),
thresholdCorrectionFactor=dict(
argstr='--thresholdCorrectionFactor %f', ),
)
inputs = BRAINSROIAuto.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 CreateLandmarkInitializeWorkflow(WFname, master_config, InterpolationMode, PostACPCAlignToAtlas, DoReverseInit, useEMSP=False, Debug=False):
CLUSTER_QUEUE=master_config['queue']
CLUSTER_QUEUE_LONG=master_config['long_q']
landmarkInitializeWF = pe.Workflow(name=WFname)
#############
inputsSpec = pe.Node(interface=IdentityInterface(fields=['inputVolume',
'atlasLandmarkFilename',
'atlasWeightFilename',
'LLSModel',
'inputTemplateModel',
'atlasVolume',
'EMSP']),
run_without_submitting=True,
name='inputspec')
#############
outputsSpec = pe.Node(interface=IdentityInterface(fields=['outputLandmarksInACPCAlignedSpace',
'outputResampledVolume', 'outputResampledCroppedVolume',
'outputLandmarksInInputSpace',
'writeBranded2DImage',
'outputTransform', 'outputMRML', 'atlasToSubjectTransform'
]),
run_without_submitting=True,
name='outputspec')
########################################################/
# Run ACPC Detect on first T1 Image - Base Image
########################################################
BCD = pe.Node(interface=BRAINSConstellationDetector(), name="BCD")
many_cpu_BCD_options_dictionary = {'qsub_args': modify_qsub_args(CLUSTER_QUEUE_LONG,4,2,4), 'overwrite': True}
BCD.plugin_args = many_cpu_BCD_options_dictionary
## Use program default BCD.inputs.inputTemplateModel = T1ACPCModelFile
# BCD.inputs.outputVolume = "BCD_OUT" + "_ACPC_InPlace.nii.gz" #$# T1AcpcImageList
BCD.inputs.outputTransform = "BCD" + "_Original2ACPC_transform.h5"
BCD.inputs.outputResampledVolume = "BCD" + "_ACPC.nii.gz"
BCD.inputs.outputLandmarksInInputSpace = "BCD" + "_Original.fcsv"
BCD.inputs.outputLandmarksInACPCAlignedSpace = "BCD" + "_ACPC_Landmarks.fcsv"
BCD.inputs.writeBranded2DImage = "BCD"+"_Branded2DQCimage.png"
# BCD.inputs.outputMRML = "BCD" + "_Scene.mrml"
BCD.inputs.interpolationMode = InterpolationMode
BCD.inputs.houghEyeDetectorMode = 1 # Look for dark eyes like on a T1 image, 0=Look for bright eyes like in a T2 image
BCD.inputs.acLowerBound = 80.0 # Chop the data set 80mm below the AC PC point.
# Entries below are of the form:
landmarkInitializeWF.connect(inputsSpec, 'inputVolume', BCD, 'inputVolume')
landmarkInitializeWF.connect(inputsSpec, 'atlasWeightFilename', BCD, 'atlasLandmarkWeights')
landmarkInitializeWF.connect(inputsSpec, 'atlasLandmarkFilename',BCD, 'atlasLandmarks')
landmarkInitializeWF.connect(inputsSpec, 'LLSModel', BCD, 'LLSModel')
landmarkInitializeWF.connect(inputsSpec, 'inputTemplateModel', BCD, 'inputTemplateModel')
# If EMSP, pre-selected landmarks are given, force to use.
if useEMSP:
print("*** Use pre-selected landmark file for Landmark Detection")
landmarkInitializeWF.connect(inputsSpec, 'EMSP', BCD, 'inputLandmarksEMSP')
# If the atlas volume is from this subject (i.e. after template building for the longitudinal phase) then set this to True
# Otherwise, it is probably best to let the ACPC alignment be fully defined by the landmark points themselves.
if PostACPCAlignToAtlas:
landmarkInitializeWF.connect(inputsSpec, 'atlasVolume', BCD, 'atlasVolume')
########################################################
# Run BLI atlas_to_subject
########################################################
BLI = pe.Node(interface=BRAINSLandmarkInitializer(), name="BLI")
BLI.inputs.outputTransformFilename = "landmarkInitializer_atlas_to_subject_transform.h5"
landmarkInitializeWF.connect(inputsSpec, 'atlasWeightFilename', BLI, 'inputWeightFilename')
landmarkInitializeWF.connect(inputsSpec, 'atlasLandmarkFilename', BLI, 'inputMovingLandmarkFilename')
landmarkInitializeWF.connect(BCD, 'outputLandmarksInACPCAlignedSpace', BLI, 'inputFixedLandmarkFilename')
## This is for debugging purposes, and it is not intended for general use.
if DoReverseInit == True:
########################################################
# Run BLI subject_to_atlas
########################################################
BLI2Atlas = pe.Node(interface=BRAINSLandmarkInitializer(), name="BLI2Atlas")
BLI2Atlas.inputs.outputTransformFilename = "landmarkInitializer_subject_to_atlas_transform.h5"
landmarkInitializeWF.connect(inputsSpec, 'atlasWeightFilename', BLI2Atlas, 'inputWeightFilename')
landmarkInitializeWF.connect(inputsSpec, 'atlasLandmarkFilename', BLI2Atlas, 'inputFixedLandmarkFilename')
landmarkInitializeWF.connect(BCD, 'outputLandmarksInInputSpace', BLI2Atlas, 'inputMovingLandmarkFilename')
Resample2Atlas = pe.Node(interface=BRAINSResample(), name="Resample2Atlas")
Resample2Atlas.inputs.interpolationMode = "Linear"
Resample2Atlas.inputs.outputVolume = "subject2atlas.nii.gz"
landmarkInitializeWF.connect(inputsSpec, 'inputVolume', Resample2Atlas, 'inputVolume')
landmarkInitializeWF.connect(BLI2Atlas, 'outputTransformFilename', Resample2Atlas, 'warpTransform')
if (DoReverseInit == True) and (Debug == True):
ResampleFromAtlas = pe.Node(interface=BRAINSResample(), name="ResampleFromAtlas")
ResampleFromAtlas.inputs.interpolationMode = "Linear"
ResampleFromAtlas.inputs.outputVolume = "atlas2subject.nii.gz"
landmarkInitializeWF.connect(inputsSpec, 'atlasVolume', ResampleFromAtlas, 'inputVolume')
landmarkInitializeWF.connect(BLI, 'outputTransformFilename', ResampleFromAtlas, 'warpTransform')
landmarkInitializeWF.connect(BCD, 'outputResampledVolume', ResampleFromAtlas, 'referenceVolume')
BROIAUTO = pe.Node(interface=BRAINSROIAuto(), name="BROIAuto_cropped")
many_cpu_BROIAUTO_options_dictionary = {'qsub_args': modify_qsub_args(CLUSTER_QUEUE_LONG,4,2,4), 'overwrite': True}
BROIAUTO.plugin_args = many_cpu_BROIAUTO_options_dictionary
BROIAUTO.inputs.outputVolume = "Cropped_BCD_ACPC_Aligned.nii.gz"
BROIAUTO.inputs.ROIAutoDilateSize = 10
BROIAUTO.inputs.cropOutput = True
landmarkInitializeWF.connect(BCD, 'outputResampledVolume', BROIAUTO, 'inputVolume')
landmarkInitializeWF.connect(BROIAUTO, 'outputVolume', outputsSpec, 'outputResampledCroppedVolume')
landmarkInitializeWF.connect(BCD, 'outputLandmarksInACPCAlignedSpace', outputsSpec, 'outputLandmarksInACPCAlignedSpace')
landmarkInitializeWF.connect(BCD, 'outputResampledVolume', outputsSpec, 'outputResampledVolume')
landmarkInitializeWF.connect(BCD, 'outputLandmarksInInputSpace', outputsSpec, 'outputLandmarksInInputSpace')
landmarkInitializeWF.connect(BCD, 'outputTransform', outputsSpec, 'outputTransform')
landmarkInitializeWF.connect(BCD, 'outputMRML', outputsSpec, 'outputMRML')
landmarkInitializeWF.connect(BCD, 'writeBranded2DImage', outputsSpec, 'writeBranded2DImage')
landmarkInitializeWF.connect(BLI, 'outputTransformFilename', outputsSpec, 'atlasToSubjectTransform')
return landmarkInitializeWF
def CreateJointFusionWorkflow(WFname, onlyT1, master_config, runFixFusionLabelMap=True):
"""
This function...
:param WFname:
:param onlyT1:
:param master_config:
:param runFixFusionLabelMap: True
:return: JointFusionWF
"""
from nipype.interfaces import ants
if onlyT1:
n_modality = 1
else:
n_modality = 2
CLUSTER_QUEUE = master_config['queue']
CLUSTER_QUEUE_LONG = master_config['long_q']
JointFusionWF = pe.Workflow(name=WFname)
inputsSpec = pe.Node(interface=IdentityInterface(fields=['subj_t1_image', # Desired image to create label map for
'subj_t2_image', # Desired image to create label map for
'subj_lmks', # The landmarks corresponding to t1_image
'subj_fixed_head_labels',
# The fixed head labels from BABC
'subj_posteriors', # The BABC posteriors
'subj_left_hemisphere', # The warped left hemisphere mask
'atlasWeightFilename', # The static weights file name
'labelBaseFilename'
# Atlas label base name ex) neuro_lbls.nii.gz
]),
run_without_submitting=True,
name='inputspec')
outputsSpec = pe.Node(interface=IdentityInterface(fields=['JointFusion_HDAtlas20_2015_label',
'JointFusion_HDAtlas20_2015_CSFVBInjected_label',
'JointFusion_HDAtlas20_2015_fs_standard_label',
'JointFusion_HDAtlas20_2015_lobe_label',
'JointFusion_extended_snapshot',
'JointFusion_HDAtlas20_2015_dustCleaned_label',
'JointFusion_volumes_csv',
'JointFusion_volumes_json',
'JointFusion_lobe_volumes_csv',
'JointFusion_lobe_volumes_json']),
run_without_submitting=True,
name='outputspec')
from collections import OrderedDict # Need OrderedDict internally to ensure consistent ordering
BLICreator = OrderedDict()
A2SantsRegistrationPreJointFusion_SyN = OrderedDict()
movingROIAuto = OrderedDict()
labelMapResample = OrderedDict()
NewlabelMapResample = OrderedDict()
jointFusion_atlas_mergeindex = 0
merge_input_offset = 1 # Merge nodes are indexed from 1, not zero!
"""
multimodal ants registration if t2 exists
"""
sessionMakeMultimodalInput = pe.Node(Function(function=MakeVector,
input_names=['inFN1', 'inFN2', 'jointFusion'],
output_names=['outFNs']),
run_without_submitting=True, name="sessionMakeMultimodalInput")
sessionMakeMultimodalInput.inputs.jointFusion = False
JointFusionWF.connect(inputsSpec, 'subj_t1_image', sessionMakeMultimodalInput, 'inFN1')
"""
T2 resample to T1 average image
:: BRAINSABC changed its behavior to retain image's original spacing & origin
:: Since antsJointFusion only works for the identical origin images for targets,
:: Resampling is placed at this stage
"""
subjectT2Resample = pe.Node(interface=BRAINSResample(), name="BRAINSResample_T2_forAntsJointFusion")
if not onlyT1:
subjectT2Resample.plugin_args = {'qsub_args': modify_qsub_args(master_config['queue'], 1, 1, 1),
'overwrite': True}
subjectT2Resample.inputs.pixelType = 'short'
subjectT2Resample.inputs.interpolationMode = 'Linear'
subjectT2Resample.inputs.outputVolume = "t2_resampled_in_t1.nii.gz"
# subjectT2Resample.inputs.warpTransform= "Identity" # Default is "Identity"
JointFusionWF.connect(inputsSpec, 'subj_t1_image', subjectT2Resample, 'referenceVolume')
JointFusionWF.connect(inputsSpec, 'subj_t2_image', subjectT2Resample, 'inputVolume')
JointFusionWF.connect(subjectT2Resample, 'outputVolume', sessionMakeMultimodalInput, 'inFN2')
else:
pass
# print('jointFusion_atlas_db_base')
print("master_config")
print(master_config)
print("master_config['jointfusion_atlas_db_base']")
print((master_config['jointfusion_atlas_db_base']))
jointFusionAtlasDict = readMalfAtlasDbBase(master_config['jointfusion_atlas_db_base'])
number_of_atlas_sources = len(jointFusionAtlasDict)
jointFusionAtlases = OrderedDict()
atlasMakeMultimodalInput = OrderedDict()
t2Resample = OrderedDict()
warpedAtlasLblMergeNode = pe.Node(interface=Merge(number_of_atlas_sources), name="LblMergeAtlas")
NewwarpedAtlasLblMergeNode = pe.Node(interface=Merge(number_of_atlas_sources), name="fswmLblMergeAtlas")
# "HACK NOT to use T2 for JointFusion only"
# warpedAtlasesMergeNode = pe.Node(interface=Merge(number_of_atlas_sources*n_modality),name="MergeAtlases")
warpedAtlasesMergeNode = pe.Node(interface=Merge(number_of_atlas_sources * 1), name="MergeAtlases")
## if using Registration masking, then do ROIAuto on fixed and moving images and connect to registraitons
UseRegistrationMasking = True
if UseRegistrationMasking == True:
from nipype.interfaces.semtools.segmentation.specialized import BRAINSROIAuto
fixedROIAuto = pe.Node(interface=BRAINSROIAuto(), name="fixedROIAUTOMask")
fixedROIAuto.inputs.ROIAutoDilateSize = 10
fixedROIAuto.inputs.outputROIMaskVolume = "fixedImageROIAutoMask.nii.gz"
JointFusionWF.connect(inputsSpec, 'subj_t1_image', fixedROIAuto, 'inputVolume')
for jointFusion_atlas_subject in list(jointFusionAtlasDict.keys()):
## Need DataGrabber Here For the Atlas
jointFusionAtlases[jointFusion_atlas_subject] = pe.Node(interface=IdentityInterface(
fields=['t1', 't2', 'label', 'lmks', 'registration_mask']),
name='jointFusionAtlasInput' + jointFusion_atlas_subject)
jointFusionAtlases[jointFusion_atlas_subject].inputs.t1 = jointFusionAtlasDict[jointFusion_atlas_subject]['t1']
jointFusionAtlases[jointFusion_atlas_subject].inputs.t2 = jointFusionAtlasDict[jointFusion_atlas_subject]['t2']
jointFusionAtlases[jointFusion_atlas_subject].inputs.label = jointFusionAtlasDict[jointFusion_atlas_subject][
'label']
jointFusionAtlases[jointFusion_atlas_subject].inputs.lmks = jointFusionAtlasDict[jointFusion_atlas_subject][
'lmks']
jointFusionAtlases[jointFusion_atlas_subject].inputs.registration_mask = \
jointFusionAtlasDict[jointFusion_atlas_subject]['registration_mask']
## Create BLI first
########################################################
# Run BLI atlas_to_subject
########################################################
BLICreator[jointFusion_atlas_subject] = pe.Node(interface=BRAINSLandmarkInitializer(),
name="BLI_" + jointFusion_atlas_subject)
BLICreator[
jointFusion_atlas_subject].inputs.outputTransformFilename = "landmarkInitializer_{0}_to_subject_transform.h5".format(
jointFusion_atlas_subject)
JointFusionWF.connect(inputsSpec, 'atlasWeightFilename', BLICreator[jointFusion_atlas_subject],
'inputWeightFilename')
JointFusionWF.connect(jointFusionAtlases[jointFusion_atlas_subject], 'lmks',
BLICreator[jointFusion_atlas_subject], 'inputMovingLandmarkFilename')
JointFusionWF.connect(inputsSpec, 'subj_lmks', BLICreator[jointFusion_atlas_subject],
'inputFixedLandmarkFilename')
##### Initialize with ANTS Transform For SyN
currentAtlasToSubjectantsRegistration = 'SyN_AtlasToSubjectANTsPreJointFusion_' + jointFusion_atlas_subject
A2SantsRegistrationPreJointFusion_SyN[jointFusion_atlas_subject] = pe.Node(interface=ants.Registration(),
name=currentAtlasToSubjectantsRegistration)
many_cpu_ANTsSyN_options_dictionary = {'qsub_args': modify_qsub_args(CLUSTER_QUEUE_LONG, 4, 2, 16),
'overwrite': True}
A2SantsRegistrationPreJointFusion_SyN[
jointFusion_atlas_subject].plugin_args = many_cpu_ANTsSyN_options_dictionary
if onlyT1:
JFregistrationTypeDescription = "FiveStageAntsRegistrationT1Only"
else:
JFregistrationTypeDescription = "FiveStageAntsRegistrationMultiModal"
CommonANTsRegistrationSettings(
antsRegistrationNode=A2SantsRegistrationPreJointFusion_SyN[jointFusion_atlas_subject],
registrationTypeDescription=JFregistrationTypeDescription,
output_transform_prefix=jointFusion_atlas_subject + '_ToSubjectPreJointFusion_SyN',
output_warped_image=jointFusion_atlas_subject + '_2subject.nii.gz',
output_inverse_warped_image=None, # NO NEED FOR THIS
save_state=None, # NO NEED FOR THIS
invert_initial_moving_transform=False,
initial_moving_transform=None)
## if using Registration masking, then do ROIAuto on fixed and moving images and connect to registraitons
if UseRegistrationMasking == True:
from nipype.interfaces.semtools.segmentation.specialized import BRAINSROIAuto
JointFusionWF.connect(fixedROIAuto, 'outputROIMaskVolume',
A2SantsRegistrationPreJointFusion_SyN[jointFusion_atlas_subject], 'fixed_image_masks')
# JointFusionWF.connect(inputsSpec, 'subj_fixed_head_labels',
# A2SantsRegistrationPreJointFusion_SyN[jointFusion_atlas_subject],'fixed_image_masks')
# NOTE: Moving image mask can be taken from Atlas directly so that it does not need to be read in
# movingROIAuto[jointFusion_atlas_subject] = pe.Node(interface=BRAINSROIAuto(), name="movingROIAUTOMask_"+jointFusion_atlas_subject)
# movingROIAuto.inputs.ROIAutoDilateSize=10
# movingROIAuto[jointFusion_atlas_subject].inputs.outputROIMaskVolume = "movingImageROIAutoMask.nii.gz"
# JointFusionWF.connect(jointFusionAtlases[jointFusion_atlas_subject], 't1', movingROIAuto[jointFusion_atlas_subject],'inputVolume')
# JointFusionWF.connect(movingROIAuto[jointFusion_atlas_subject], 'outputROIMaskVolume',A2SantsRegistrationPreJointFusion_SyN[jointFusion_atlas_subject],'moving_image_masks')
JointFusionWF.connect(jointFusionAtlases[jointFusion_atlas_subject], 'registration_mask',
A2SantsRegistrationPreJointFusion_SyN[jointFusion_atlas_subject], 'moving_image_masks')
JointFusionWF.connect(BLICreator[jointFusion_atlas_subject], 'outputTransformFilename',
A2SantsRegistrationPreJointFusion_SyN[jointFusion_atlas_subject],
'initial_moving_transform')
"""
make multimodal input for atlases
"""
atlasMakeMultimodalInput[jointFusion_atlas_subject] = pe.Node(Function(function=MakeVector,
input_names=['inFN1', 'inFN2',
'jointFusion'],
output_names=['outFNs']),
run_without_submitting=True,
name="atlasMakeMultimodalInput" + jointFusion_atlas_subject)
atlasMakeMultimodalInput[jointFusion_atlas_subject].inputs.jointFusion = False
JointFusionWF.connect(jointFusionAtlases[jointFusion_atlas_subject], 't1',
atlasMakeMultimodalInput[jointFusion_atlas_subject], 'inFN1')
if not onlyT1:
JointFusionWF.connect(jointFusionAtlases[jointFusion_atlas_subject], 't2',
atlasMakeMultimodalInput[jointFusion_atlas_subject], 'inFN2')
else:
pass
JointFusionWF.connect(sessionMakeMultimodalInput, 'outFNs',
A2SantsRegistrationPreJointFusion_SyN[jointFusion_atlas_subject], 'fixed_image')
JointFusionWF.connect(atlasMakeMultimodalInput[jointFusion_atlas_subject], 'outFNs',
A2SantsRegistrationPreJointFusion_SyN[jointFusion_atlas_subject], 'moving_image')
"HACK NOT to use T2 for JointFusion"
# JointFusionWF.connect(A2SantsRegistrationPreJointFusion_SyN[jointFusion_atlas_subject],'warped_image',
# warpedAtlasesMergeNode,'in'+str(merge_input_offset + jointFusion_atlas_mergeindex*n_modality) )
JointFusionWF.connect(A2SantsRegistrationPreJointFusion_SyN[jointFusion_atlas_subject], 'warped_image',
warpedAtlasesMergeNode, 'in' + str(merge_input_offset + jointFusion_atlas_mergeindex * 1))
"""
Original t2 resampling
"""
for modality_index in range(1, n_modality):
t2Resample[jointFusion_atlas_subject] = pe.Node(interface=ants.ApplyTransforms(),
name="resampledT2" + jointFusion_atlas_subject)
many_cpu_t2Resample_options_dictionary = {'qsub_args': modify_qsub_args(CLUSTER_QUEUE, 1, 1, 1),
'overwrite': True}
t2Resample[jointFusion_atlas_subject].plugin_args = many_cpu_t2Resample_options_dictionary
t2Resample[jointFusion_atlas_subject].inputs.num_threads = -1
t2Resample[jointFusion_atlas_subject].inputs.dimension = 3
t2Resample[jointFusion_atlas_subject].inputs.output_image = jointFusion_atlas_subject + '_t2.nii.gz'
t2Resample[jointFusion_atlas_subject].inputs.interpolation = 'BSpline'
t2Resample[jointFusion_atlas_subject].inputs.default_value = 0
t2Resample[jointFusion_atlas_subject].inputs.invert_transform_flags = [False]
JointFusionWF.connect(A2SantsRegistrationPreJointFusion_SyN[jointFusion_atlas_subject],
'composite_transform',
t2Resample[jointFusion_atlas_subject], 'transforms')
JointFusionWF.connect(inputsSpec, 'subj_t1_image',
t2Resample[jointFusion_atlas_subject], 'reference_image')
JointFusionWF.connect(jointFusionAtlases[jointFusion_atlas_subject], 't2',
t2Resample[jointFusion_atlas_subject], 'input_image')
"HACK NOT to use T2 for JointFusion only"
# JointFusionWF.connect(t2Resample[jointFusion_atlas_subject],'output_image',
# warpedAtlasesMergeNode,'in'+str(merge_input_offset + jointFusion_atlas_mergeindex*n_modality+modality_index) )
"""
Original labelmap resampling
"""
labelMapResample[jointFusion_atlas_subject] = pe.Node(interface=ants.ApplyTransforms(),
name="resampledLabel" + jointFusion_atlas_subject)
many_cpu_labelMapResample_options_dictionary = {'qsub_args': modify_qsub_args(CLUSTER_QUEUE, 1, 1, 1),
'overwrite': True}
labelMapResample[jointFusion_atlas_subject].plugin_args = many_cpu_labelMapResample_options_dictionary
labelMapResample[jointFusion_atlas_subject].inputs.num_threads = -1
labelMapResample[jointFusion_atlas_subject].inputs.dimension = 3
labelMapResample[
jointFusion_atlas_subject].inputs.output_image = jointFusion_atlas_subject + '_2_subj_lbl.nii.gz'
labelMapResample[jointFusion_atlas_subject].inputs.interpolation = 'MultiLabel'
labelMapResample[jointFusion_atlas_subject].inputs.default_value = 0
labelMapResample[jointFusion_atlas_subject].inputs.invert_transform_flags = [False]
JointFusionWF.connect(A2SantsRegistrationPreJointFusion_SyN[jointFusion_atlas_subject], 'composite_transform',
labelMapResample[jointFusion_atlas_subject], 'transforms')
JointFusionWF.connect(inputsSpec, 'subj_t1_image',
labelMapResample[jointFusion_atlas_subject], 'reference_image')
JointFusionWF.connect(jointFusionAtlases[jointFusion_atlas_subject], 'label',
labelMapResample[jointFusion_atlas_subject], 'input_image')
JointFusionWF.connect(labelMapResample[jointFusion_atlas_subject], 'output_image', warpedAtlasLblMergeNode,
'in' + str(merge_input_offset + jointFusion_atlas_mergeindex))
### New labelmap resampling
NewlabelMapResample[jointFusion_atlas_subject] = pe.Node(interface=ants.ApplyTransforms(),
name="FSWM_WLABEL_" + jointFusion_atlas_subject)
many_cpu_NewlabelMapResample_options_dictionary = {'qsub_args': modify_qsub_args(CLUSTER_QUEUE, 1, 1, 1),
'overwrite': True}
NewlabelMapResample[jointFusion_atlas_subject].plugin_args = many_cpu_NewlabelMapResample_options_dictionary
NewlabelMapResample[jointFusion_atlas_subject].inputs.num_threads = -1
NewlabelMapResample[jointFusion_atlas_subject].inputs.dimension = 3
NewlabelMapResample[
jointFusion_atlas_subject].inputs.output_image = jointFusion_atlas_subject + 'fswm_2_subj_lbl.nii.gz'
NewlabelMapResample[jointFusion_atlas_subject].inputs.interpolation = 'MultiLabel'
NewlabelMapResample[jointFusion_atlas_subject].inputs.default_value = 0
NewlabelMapResample[jointFusion_atlas_subject].inputs.invert_transform_flags = [False]
JointFusionWF.connect(A2SantsRegistrationPreJointFusion_SyN[jointFusion_atlas_subject], 'composite_transform',
NewlabelMapResample[jointFusion_atlas_subject], 'transforms')
JointFusionWF.connect(inputsSpec, 'subj_t1_image',
NewlabelMapResample[jointFusion_atlas_subject], 'reference_image')
JointFusionWF.connect(jointFusionAtlases[jointFusion_atlas_subject], 'label',
NewlabelMapResample[jointFusion_atlas_subject], 'input_image')
JointFusionWF.connect(NewlabelMapResample[jointFusion_atlas_subject], 'output_image',
NewwarpedAtlasLblMergeNode, 'in' + str(merge_input_offset + jointFusion_atlas_mergeindex))
jointFusion_atlas_mergeindex += 1
## Now work on cleaning up the label maps
from .FixLabelMapsTools import FixLabelMapFromNeuromorphemetrics2012
from .FixLabelMapsTools import RecodeLabelMap
### Original NeuroMorphometrica merged fusion
jointFusion = pe.Node(interface=ants.AntsJointFusion(), name="AntsJointFusion")
many_cpu_JointFusion_options_dictionary = {'qsub_args': modify_qsub_args(CLUSTER_QUEUE, 10, 8, 16),
'overwrite': True}
jointFusion.plugin_args = many_cpu_JointFusion_options_dictionary
jointFusion.inputs.num_threads = -1
jointFusion.inputs.dimension = 3
jointFusion.inputs.search_radius = [3]
# jointFusion.inputs.method='Joint[0.1,2]'
jointFusion.inputs.out_label_fusion = 'JointFusion_HDAtlas20_2015_label.nii.gz'
# JointFusionWF.connect(inputsSpec, 'subj_fixed_head_labels', jointFusion, 'mask_image')
JointFusionWF.connect(fixedROIAuto, 'outputROIMaskVolume', jointFusion, 'mask_image')
JointFusionWF.connect(warpedAtlasLblMergeNode, 'out',
jointFusion, 'atlas_segmentation_image')
AdjustMergeListNode = pe.Node(Function(function=adjustMergeList,
input_names=['allList', 'n_modality'],
output_names=['out']),
name="AdjustMergeListNode")
"*** HACK JointFusion only uses T1"
# AdjustMergeListNode.inputs.n_modality = n_modality
AdjustMergeListNode.inputs.n_modality = 1
JointFusionWF.connect(warpedAtlasesMergeNode, 'out', AdjustMergeListNode, 'allList')
JointFusionWF.connect(AdjustMergeListNode, 'out', jointFusion, 'atlas_image')
AdjustTargetImageListNode = pe.Node(Function(function=adjustMergeList,
input_names=['allList', 'n_modality'],
output_names=['out']),
name="AdjustTargetImageListNode")
AdjustTargetImageListNode.inputs.n_modality = n_modality
"*** HACK JointFusion only uses T1"
""" Once JointFusion works with T2 properly,
delete sessionMakeListSingleModalInput and use sessionMakeMultimodalInput instead
"""
sessionMakeListSingleModalInput = pe.Node(Function(function=MakeVector,
input_names=['inFN1', 'inFN2', 'jointFusion'],
output_names=['outFNs']),
run_without_submitting=True, name="sessionMakeListSingleModalInput")
sessionMakeListSingleModalInput.inputs.jointFusion = False
JointFusionWF.connect(inputsSpec, 'subj_t1_image', sessionMakeListSingleModalInput, 'inFN1')
JointFusionWF.connect(sessionMakeListSingleModalInput, 'outFNs', jointFusion, 'target_image')
JointFusionWF.connect(jointFusion, 'out_label_fusion', outputsSpec, 'JointFusion_HDAtlas20_2015_label')
## We need to recode values to ensure that the labels match FreeSurer as close as possible by merging
## some labels together to standard FreeSurfer confenventions (i.e. for WMQL)
RECODE_LABELS_2_Standard_FSWM = [
(15071, 47), (15072, 47), (15073, 47), (15145, 1011), (15157, 1011), (15161, 1011),
(15179, 1012), (15141, 1014), (15151, 1017), (15163, 1018), (15165, 1019), (15143, 1027),
(15191, 1028), (15193, 1028), (15185, 1030), (15201, 1030), (15175, 1031), (15195, 1031),
(15173, 1035), (15144, 2011), (15156, 2011), (15160, 2011), (15178, 2012), (15140, 2014),
(15150, 2017), (15162, 2018), (15164, 2019), (15142, 2027), (15190, 2028), (15192, 2028),
(15184, 2030), (15174, 2031), (15194, 2031), (15172, 2035), (15200, 2030)]
## def RecodeLabelMap(InputFileName,OutputFileName,RECODE_TABLE):
RecodeToStandardFSWM = pe.Node(Function(function=RecodeLabelMap,
input_names=['InputFileName', 'OutputFileName', 'RECODE_TABLE'],
output_names=['OutputFileName']),
name="RecodeToStandardFSWM")
RecodeToStandardFSWM.inputs.RECODE_TABLE = RECODE_LABELS_2_Standard_FSWM
RecodeToStandardFSWM.inputs.OutputFileName = 'JointFusion_HDAtlas20_2015_fs_standard_label.nii.gz'
JointFusionWF.connect(RecodeToStandardFSWM, 'OutputFileName', outputsSpec,
'JointFusion_HDAtlas20_2015_fs_standard_label')
## JointFusion_SNAPSHOT_WRITER for Segmented result checking:
# JointFusion_SNAPSHOT_WRITERNodeName = "JointFusion_ExtendedJointFusion_SNAPSHOT_WRITER"
# JointFusion_SNAPSHOT_WRITER = pe.Node(interface=BRAINSSnapShotWriter(), name=JointFusion_SNAPSHOT_WRITERNodeName)
# JointFusion_SNAPSHOT_WRITER.inputs.outputFilename = 'JointFusion_HDAtlas20_2015_CSFVBInjected_label.png' # output specification
# JointFusion_SNAPSHOT_WRITER.inputs.inputPlaneDirection = [2, 1, 1, 1, 1, 0, 0]
# JointFusion_SNAPSHOT_WRITER.inputs.inputSliceToExtractInPhysicalPoint = [-3, -7, -3, 5, 7, 22, -22]
# JointFusionWF.connect(JointFusion_SNAPSHOT_WRITER,'outputFilename',outputsSpec,'JointFusion_extended_snapshot')
myLocalDustCleanup = CreateDustCleanupWorkflow("DUST_CLEANUP", onlyT1, master_config)
JointFusionWF.connect(inputsSpec, 'subj_t1_image', myLocalDustCleanup, 'inputspec.subj_t1_image')
if not onlyT1:
JointFusionWF.connect(subjectT2Resample, 'outputVolume', myLocalDustCleanup, 'inputspec.subj_t2_image')
if runFixFusionLabelMap:
## post processing of jointfusion
injectSurfaceCSFandVBIntoLabelMap = pe.Node(Function(function=FixLabelMapFromNeuromorphemetrics2012,
input_names=['fusionFN',
'FixedHeadFN',
'posteriorListOfTuples',
'LeftHemisphereFN',
'outFN',
'OUT_DICT'],
output_names=['fixedFusionLabelFN']),
name="injectSurfaceCSFandVBIntoLabelMap")
injectSurfaceCSFandVBIntoLabelMap.inputs.outFN = 'JointFusion_HDAtlas20_2015_CSFVBInjected_label.nii.gz'
from collections import OrderedDict # Need OrderedDict internally to ensure consistent ordering
FREESURFER_DICT = OrderedDict({'BRAINSTEM': 16, 'RH_CSF': 24, 'LH_CSF': 24, 'BLOOD': 15000, 'UNKNOWN': 999,
'CONNECTED': [11, 12, 13, 9, 17, 26, 50, 51, 52, 48, 53, 58]
})
injectSurfaceCSFandVBIntoLabelMap.inputs.OUT_DICT = FREESURFER_DICT
JointFusionWF.connect(jointFusion, 'out_label_fusion', injectSurfaceCSFandVBIntoLabelMap, 'fusionFN')
JointFusionWF.connect(inputsSpec, 'subj_fixed_head_labels', injectSurfaceCSFandVBIntoLabelMap, 'FixedHeadFN')
JointFusionWF.connect(inputsSpec, 'subj_posteriors', injectSurfaceCSFandVBIntoLabelMap, 'posteriorListOfTuples')
JointFusionWF.connect(inputsSpec, 'subj_left_hemisphere', injectSurfaceCSFandVBIntoLabelMap, 'LeftHemisphereFN')
JointFusionWF.connect(injectSurfaceCSFandVBIntoLabelMap, 'fixedFusionLabelFN',
myLocalDustCleanup, 'inputspec.subj_label_atlas')
JointFusionWF.connect(injectSurfaceCSFandVBIntoLabelMap, 'fixedFusionLabelFN',
outputsSpec, 'JointFusion_HDAtlas20_2015_CSFVBInjected_label')
JointFusionWF.connect(myLocalDustCleanup, 'outputspec.JointFusion_HDAtlas20_2015_dustCleaned_label',
RecodeToStandardFSWM, 'InputFileName')
JointFusionWF.connect(myLocalDustCleanup, 'outputspec.JointFusion_HDAtlas20_2015_dustCleaned_label',
outputsSpec, 'JointFusion_HDAtlas20_2015_dustCleaned_label')
# JointFusionWF.connect([(inputsSpec, JointFusion_SNAPSHOT_WRITER, [( 'subj_t1_image','inputVolumes')]),
# (injectSurfaceCSFandVBIntoLabelMap, JointFusion_SNAPSHOT_WRITER,
# [('fixedFusionLabelFN', 'inputBinaryVolumes')])
# ])
else:
JointFusionWF.connect(jointFusion, 'output_label_image',
myLocalDustCleanup, 'inputspec.subj_label_atlas')
JointFusionWF.connect(jointFusion, 'output_label_image',
outputsSpec, 'JointFusion_HDAtlas20_2015_CSFVBInjected_label')
JointFusionWF.connect(myLocalDustCleanup, 'outputspec.JointFusion_HDAtlas20_2015_dustCleaned_label',
RecodeToStandardFSWM, 'InputFileName')
JointFusionWF.connect(myLocalDustCleanup, 'outputspec.JointFusion_HDAtlas20_2015_dustCleaned_label',
outputsSpec, 'JointFusion_HDAtlas20_2015_dustCleaned_label')
# JointFusionWF.connect([(inputsSpec, JointFusion_SNAPSHOT_WRITER, [( 'subj_t1_image','inputVolumes')]),
# (jointFusion, JointFusion_SNAPSHOT_WRITER,
# [('output_label_image', 'inputBinaryVolumes')])
# ])
"""
Compute label volumes
"""
computeLabelVolumes = CreateVolumeMeasureWorkflow("LabelVolume", master_config)
JointFusionWF.connect(inputsSpec, 'subj_t1_image',
computeLabelVolumes, 'inputspec.subj_t1_image')
JointFusionWF.connect(myLocalDustCleanup, 'outputspec.JointFusion_HDAtlas20_2015_dustCleaned_label',
computeLabelVolumes, 'inputspec.subj_label_image')
JointFusionWF.connect(computeLabelVolumes, 'outputspec.csvFilename',
outputsSpec, 'JointFusion_volumes_csv')
JointFusionWF.connect(computeLabelVolumes, 'outputspec.jsonFilename',
outputsSpec, 'JointFusion_volumes_json')
## Lobe Pacellation by recoding
if master_config['relabel2lobes_filename'] != None:
# print("Generate relabeled version based on {0}".format(master_config['relabel2lobes_filename']))
RECODE_LABELS_2_LobePacellation = readRecodingList(master_config['relabel2lobes_filename'])
RecordToFSLobes = pe.Node(Function(function=RecodeLabelMap,
input_names=['InputFileName', 'OutputFileName', 'RECODE_TABLE'],
output_names=['OutputFileName']),
name="RecordToFSLobes")
RecordToFSLobes.inputs.RECODE_TABLE = RECODE_LABELS_2_LobePacellation
RecordToFSLobes.inputs.OutputFileName = 'JointFusion_HDAtlas20_2015_lobe_label.nii.gz'
JointFusionWF.connect(RecodeToStandardFSWM, 'OutputFileName', RecordToFSLobes, 'InputFileName')
JointFusionWF.connect(RecordToFSLobes, 'OutputFileName', outputsSpec, 'JointFusion_HDAtlas20_2015_lobe_label')
"""
Compute lobe volumes
"""
computeLobeVolumes = CreateVolumeMeasureWorkflow("LobeVolume", master_config)
JointFusionWF.connect(inputsSpec, 'subj_t1_image',
computeLobeVolumes, 'inputspec.subj_t1_image')
JointFusionWF.connect(RecordToFSLobes, 'OutputFileName',
computeLobeVolumes, 'inputspec.subj_label_image')
JointFusionWF.connect(computeLobeVolumes, 'outputspec.csvFilename',
outputsSpec, 'JointFusion_lobe_volumes_csv')
JointFusionWF.connect(computeLobeVolumes, 'outputspec.jsonFilename',
outputsSpec, 'JointFusion_lobe_volumes_json')
return JointFusionWF
def CreateTissueClassifyWorkflow(WFname, master_config, InterpolationMode,
UseRegistrationMasking):
from nipype.interfaces import ants
CLUSTER_QUEUE = master_config['queue']
CLUSTER_QUEUE_LONG = master_config['long_q']
tissueClassifyWF = pe.Workflow(name=WFname)
inputsSpec = pe.Node(interface=IdentityInterface(fields=[
'T1List', 'T2List', 'PDList', 'FLList', 'OTHERList', 'T1_count',
'PrimaryT1', 'atlasDefinition', 'atlasToSubjectInitialTransform',
'atlasVolume', 'atlasheadregion'
]),
run_without_submitting=True,
name='inputspec')
outputsSpec = pe.Node(
interface=IdentityInterface(fields=[
'atlasToSubjectTransform',
'atlasToSubjectInverseTransform',
'atlasToSubjectRegistrationState',
'outputLabels',
'outputHeadLabels', # ???
#'t1_corrected', 't2_corrected',
't1_average',
't2_average',
'pd_average',
'fl_average',
'posteriorImages',
]),
run_without_submitting=True,
name='outputspec')
########################################################
# Run BABCext on Multi-modal images
########################################################
makeOutImageList = pe.Node(Function(function=MakeOutFileList,
input_names=[
'T1List', 'T2List', 'PDList',
'FLList', 'OTHERList', 'postfix',
'postfixBFC', 'postfixUnwrapped',
'PrimaryT1', 'ListOutType'
],
output_names=[
'inImageList', 'outImageList',
'outBFCImageList',
'outUnwrappedImageList',
'imageTypeList'
]),
run_without_submitting=True,
name="99_makeOutImageList")
tissueClassifyWF.connect(inputsSpec, 'T1List', makeOutImageList, 'T1List')
tissueClassifyWF.connect(inputsSpec, 'T2List', makeOutImageList, 'T2List')
tissueClassifyWF.connect(inputsSpec, 'PDList', makeOutImageList, 'PDList')
tissueClassifyWF.connect(inputsSpec, 'FLList', makeOutImageList, 'FLList')
tissueClassifyWF.connect(inputsSpec, 'OTHERList', makeOutImageList,
'OTHERList')
tissueClassifyWF.connect(inputsSpec, 'PrimaryT1', makeOutImageList,
'PrimaryT1')
makeOutImageList.inputs.ListOutType = False
makeOutImageList.inputs.postfix = "_corrected.nii.gz"
makeOutImageList.inputs.postfixBFC = "_NOT_USED"
makeOutImageList.inputs.postfixUnwrapped = "_NOT_USED"
##### Initialize with ANTS Transform For AffineComponentBABC
currentAtlasToSubjectantsRigidRegistration = 'AtlasToSubjectANTsPreABC_Affine'
A2SantsRegistrationPreABCAffine = pe.Node(
interface=ants.Registration(),
name=currentAtlasToSubjectantsRigidRegistration)
many_cpu_ANTsRigid_options_dictionary = {
'qsub_args': modify_qsub_args(CLUSTER_QUEUE, 4, 2, 8),
'overwrite': True
}
A2SantsRegistrationPreABCAffine.plugin_args = many_cpu_ANTsRigid_options_dictionary
CommonANTsRegistrationSettings(
antsRegistrationNode=A2SantsRegistrationPreABCAffine,
registrationTypeDescription='AtlasToSubjectANTsPreABC_Affine',
output_transform_prefix='AtlasToSubjectPreBABC_Rigid',
output_warped_image='atlas2subjectRigid.nii.gz',
output_inverse_warped_image='subject2atlasRigid.nii.gz',
save_state=None,
invert_initial_moving_transform=False)
tissueClassifyWF.connect(inputsSpec, 'atlasToSubjectInitialTransform',
A2SantsRegistrationPreABCAffine,
'initial_moving_transform')
tissueClassifyWF.connect(inputsSpec, 'PrimaryT1',
A2SantsRegistrationPreABCAffine, 'fixed_image')
tissueClassifyWF.connect(inputsSpec, 'atlasVolume',
A2SantsRegistrationPreABCAffine, 'moving_image')
##### Initialize with ANTS Transform For SyN component BABC
currentAtlasToSubjectantsRegistration = 'AtlasToSubjectANTsPreABC_SyN'
A2SantsRegistrationPreABCSyN = pe.Node(
interface=ants.Registration(),
name=currentAtlasToSubjectantsRegistration)
many_cpu_ANTsSyN_options_dictionary = {
'qsub_args': modify_qsub_args(CLUSTER_QUEUE_LONG, 8, 8, 16),
'overwrite': True
}
A2SantsRegistrationPreABCSyN.plugin_args = many_cpu_ANTsSyN_options_dictionary
CommonANTsRegistrationSettings(
antsRegistrationNode=A2SantsRegistrationPreABCSyN,
registrationTypeDescription='AtlasToSubjectANTsPreABC_SyN',
output_transform_prefix='AtlasToSubjectPreBABC_SyN',
output_warped_image='atlas2subject.nii.gz',
output_inverse_warped_image='subject2atlas.nii.gz',
save_state='SavedInternalSyNState.h5',
invert_initial_moving_transform=False)
## if using Registration masking, then do ROIAuto on fixed and moving images and connect to registraitons
if UseRegistrationMasking == True:
from nipype.interfaces.semtools.segmentation.specialized import BRAINSROIAuto
fixedROIAuto = pe.Node(interface=BRAINSROIAuto(),
name="fixedImageROIAUTOMask")
fixedROIAuto.inputs.ROIAutoDilateSize = 15 ## NOTE Very large to include some skull in bad cases of bias where back of head is very dark
fixedROIAuto.inputs.outputROIMaskVolume = "fixedImageROIAutoMask.nii.gz"
tissueClassifyWF.connect(inputsSpec, 'PrimaryT1', fixedROIAuto,
'inputVolume')
tissueClassifyWF.connect(fixedROIAuto, 'outputROIMaskVolume',
A2SantsRegistrationPreABCAffine,
'fixed_image_mask')
tissueClassifyWF.connect(fixedROIAuto, 'outputROIMaskVolume',
A2SantsRegistrationPreABCSyN,
'fixed_image_mask')
## NOTE: Always use atlas head region to avoid computing this every time.
tissueClassifyWF.connect(inputsSpec, 'atlasheadregion',
A2SantsRegistrationPreABCAffine,
'moving_image_mask')
tissueClassifyWF.connect(inputsSpec, 'atlasheadregion',
A2SantsRegistrationPreABCSyN, 'moving_image_mask')
tissueClassifyWF.connect(A2SantsRegistrationPreABCAffine,
'composite_transform',
A2SantsRegistrationPreABCSyN,
'initial_moving_transform')
tissueClassifyWF.connect(inputsSpec, 'PrimaryT1',
A2SantsRegistrationPreABCSyN, 'fixed_image')
tissueClassifyWF.connect(inputsSpec, 'atlasVolume',
A2SantsRegistrationPreABCSyN, 'moving_image')
BABCext = pe.Node(interface=BRAINSABCext(), name="BABC")
many_cpu_BABC_options_dictionary = {
'qsub_args': modify_qsub_args(CLUSTER_QUEUE, 13, 8, 16),
'overwrite': True
}
BABCext.plugin_args = many_cpu_BABC_options_dictionary
tissueClassifyWF.connect(makeOutImageList, 'inImageList', BABCext,
'inputVolumes')
tissueClassifyWF.connect(makeOutImageList, 'imageTypeList', BABCext,
'inputVolumeTypes')
tissueClassifyWF.connect(makeOutImageList, 'outImageList', BABCext,
'outputVolumes')
BABCext.inputs.debuglevel = 0
BABCext.inputs.useKNN = True
BABCext.inputs.purePlugsThreshold = 0.1 #New feature to allow for pure plug processing and improvements.
BABCext.inputs.maxIterations = 2
BABCext.inputs.maxBiasDegree = 0
BABCext.inputs.filterIteration = 3
#BABCext.inputs.filterMethod = 'GradientAnisotropicDiffusion' ## If inputs are denoised, we don't need this
BABCext.inputs.filterMethod = 'None'
BABCext.inputs.atlasToSubjectTransformType = 'SyN'
# Using SyN, so no bsplines here BABCext.inputs.gridSize = [10, 10, 10]
BABCext.inputs.outputFormat = "NIFTI"
BABCext.inputs.outputLabels = "brain_label_seg.nii.gz"
BABCext.inputs.outputDirtyLabels = "volume_label_seg.nii.gz"
BABCext.inputs.posteriorTemplate = "POSTERIOR_%s.nii.gz"
BABCext.inputs.atlasToSubjectTransform = "atlas_to_subject.h5"
# BABCext.inputs.implicitOutputs = ['t1_average_BRAINSABC.nii.gz', 't2_average_BRAINSABC.nii.gz']
BABCext.inputs.interpolationMode = InterpolationMode
BABCext.inputs.outputDir = './'
BABCext.inputs.saveState = 'SavedBABCInternalSyNState.h5'
tissueClassifyWF.connect(inputsSpec, 'atlasDefinition', BABCext,
'atlasDefinition')
# NOTE: MUTUALLY EXCLUSIVE with restoreState
#tissueClassifyWF.connect(A2SantsRegistrationPreABCSyN,
# 'composite_transform',
# BABCext, 'atlasToSubjectInitialTransform')
tissueClassifyWF.connect(A2SantsRegistrationPreABCSyN, 'save_state',
BABCext, 'restoreState')
"""
Get the first T1 and T2 corrected images from BABCext
"""
""" HACK: THIS IS NOT NEEDED! We should use the averged t1 and averaged t2 images instead!
def get_first_T1_and_T2(in_files,T1_count):
'''
Returns the first T1 and T2 file in in_files, based on offset in T1_count.
'''
return in_files[0],in_files[T1_count]
bfc_files = pe.Node(Function(input_names=['in_files','T1_count'],
output_names=['t1_corrected','t2_corrected'],
function=get_first_T1_and_T2), run_without_submitting=True, name='99_bfc_files' )
tissueClassifyWF.connect( inputsSpec, 'T1_count', bfc_files, 'T1_count')
tissueClassifyWF.connect(BABCext,'outputVolumes',bfc_files, 'in_files')
tissueClassifyWF.connect(bfc_files,'t1_corrected',outputsSpec,'t1_corrected')
tissueClassifyWF.connect(bfc_files,'t2_corrected',outputsSpec,'t2_corrected')
#tissueClassifyWF.connect(bfc_files,'pd_corrected',outputsSpec,'pd_corrected')
#tissueClassifyWF.connect(bfc_files,'fl_corrected',outputsSpec,'fl_corrected')
"""
#############
tissueClassifyWF.connect(BABCext, 'saveState', outputsSpec,
'atlasToSubjectRegistrationState')
tissueClassifyWF.connect(BABCext, 'atlasToSubjectTransform', outputsSpec,
'atlasToSubjectTransform')
def MakeInverseTransformFileName(TransformFileName):
"""### HACK: This function is to work around a deficiency in BRAINSABCext where the inverse transform name is not being computed properly
in the list outputs"""
fixed_inverse_name = TransformFileName.replace(".h5", "_Inverse.h5")
return [fixed_inverse_name]
tissueClassifyWF.connect([
(BABCext, outputsSpec, [(('atlasToSubjectTransform',
MakeInverseTransformFileName),
"atlasToSubjectInverseTransform")]),
])
tissueClassifyWF.connect(BABCext, 'outputLabels', outputsSpec,
'outputLabels')
tissueClassifyWF.connect(BABCext, 'outputDirtyLabels', outputsSpec,
'outputHeadLabels')
tissueClassifyWF.connect(BABCext, 'outputT1AverageImage', outputsSpec,
't1_average')
tissueClassifyWF.connect(BABCext, 'outputT2AverageImage', outputsSpec,
't2_average')
tissueClassifyWF.connect(BABCext, 'outputPDAverageImage', outputsSpec,
'pd_average')
tissueClassifyWF.connect(BABCext, 'outputFLAverageImage', outputsSpec,
'fl_average')
## remove tissueClassifyWF.connect( [ ( BABCext, outputsSpec, [ (( 'outputAverageImages', getListIndexOrNoneIfOutOfRange, 0 ), "t1_average")] ), ] )
## remove tissueClassifyWF.connect( [ ( BABCext, outputsSpec, [ (( 'outputAverageImages', getListIndexOrNoneIfOutOfRange, 1 ), "t2_average")] ), ] )
## remove tissueClassifyWF.connect( [ ( BABCext, outputsSpec, [ (( 'outputAverageImages', getListIndexOrNoneIfOutOfRange, 2 ), "pd_average")] ), ] )
MakePosteriorDictionaryNode = pe.Node(Function(
function=MakePosteriorDictionaryFunc,
input_names=['posteriorImages'],
output_names=['posteriorDictionary']),
run_without_submitting=True,
name="99_makePosteriorDictionary")
tissueClassifyWF.connect(BABCext, 'posteriorImages',
MakePosteriorDictionaryNode, 'posteriorImages')
tissueClassifyWF.connect(MakePosteriorDictionaryNode,
'posteriorDictionary', outputsSpec,
'posteriorImages')
return tissueClassifyWF
def CreateTissueClassifyWorkflow(WFname, master_config, InterpolationMode,
UseRegistrationMasking):
from nipype.interfaces import ants
CLUSTER_QUEUE = master_config['queue']
CLUSTER_QUEUE_LONG = master_config['long_q']
tissueClassifyWF = pe.Workflow(name=WFname)
inputsSpec = pe.Node(interface=IdentityInterface(fields=[
'T1List', 'T2List', 'PDList', 'FLList', 'OtherList', 'T1_count',
'PrimaryT1', 'atlasDefinition', 'atlasToSubjectInitialTransform',
'atlasVolume'
]),
run_without_submitting=True,
name='inputspec')
outputsSpec = pe.Node(
interface=IdentityInterface(fields=[
'atlasToSubjectTransform',
'atlasToSubjectInverseTransform',
'atlasToSubjectRegistrationState',
'outputLabels',
'outputHeadLabels', # ???
#'t1_corrected', 't2_corrected',
't1_average',
't2_average',
'pd_average',
'fl_average',
'posteriorImages',
]),
run_without_submitting=True,
name='outputspec')
########################################################
# Run BABCext on Multi-modal images
########################################################
makeOutImageList = pe.Node(Function(
function=MakeOutFileList,
input_names=[
'T1List', 'T2List', 'PDList', 'FLList', 'OtherList', 'postfix',
'PrimaryT1'
],
output_names=['inImageList', 'outImageList', 'imageTypeList']),
run_without_submitting=True,
name="99_makeOutImageList")
tissueClassifyWF.connect(inputsSpec, 'T1List', makeOutImageList, 'T1List')
tissueClassifyWF.connect(inputsSpec, 'T2List', makeOutImageList, 'T2List')
tissueClassifyWF.connect(inputsSpec, 'PDList', makeOutImageList, 'PDList')
tissueClassifyWF.connect(inputsSpec, 'PrimaryT1', makeOutImageList,
'PrimaryT1')
makeOutImageList.inputs.FLList = [] # an emptyList HACK
makeOutImageList.inputs.postfix = "_corrected.nii.gz"
# HACK tissueClassifyWF.connect( inputsSpec, 'FLList', makeOutImageList, 'FLList' )
tissueClassifyWF.connect(inputsSpec, 'OtherList', makeOutImageList,
'OtherList')
##### Initialize with ANTS Transform For AffineComponentBABC
currentAtlasToSubjectantsRigidRegistration = 'AtlasToSubjectANTsPreABC_Rigid'
A2SantsRegistrationPreABCRigid = pe.Node(
interface=ants.Registration(),
name=currentAtlasToSubjectantsRigidRegistration)
many_cpu_ANTsRigid_options_dictionary = {
'qsub_args': modify_qsub_args(CLUSTER_QUEUE, 4, 2, 8),
'overwrite': True
}
A2SantsRegistrationPreABCRigid.plugin_args = many_cpu_ANTsRigid_options_dictionary
A2SantsRegistrationPreABCRigid.inputs.num_threads = -1
A2SantsRegistrationPreABCRigid.inputs.dimension = 3
A2SantsRegistrationPreABCRigid.inputs.transforms = [
"Affine",
]
A2SantsRegistrationPreABCRigid.inputs.transform_parameters = [[0.1]]
A2SantsRegistrationPreABCRigid.inputs.metric = ['MI']
A2SantsRegistrationPreABCRigid.inputs.sampling_strategy = ['Regular']
A2SantsRegistrationPreABCRigid.inputs.sampling_percentage = [0.5]
A2SantsRegistrationPreABCRigid.inputs.metric_weight = [1.0]
A2SantsRegistrationPreABCRigid.inputs.radius_or_number_of_bins = [32]
A2SantsRegistrationPreABCRigid.inputs.number_of_iterations = [[
1000, 1000, 500, 100
]]
A2SantsRegistrationPreABCRigid.inputs.convergence_threshold = [1e-8]
A2SantsRegistrationPreABCRigid.inputs.convergence_window_size = [10]
A2SantsRegistrationPreABCRigid.inputs.use_histogram_matching = [True]
A2SantsRegistrationPreABCRigid.inputs.shrink_factors = [[8, 4, 2, 1]]
A2SantsRegistrationPreABCRigid.inputs.smoothing_sigmas = [[3, 2, 1, 0]]
A2SantsRegistrationPreABCRigid.inputs.sigma_units = ["vox"]
A2SantsRegistrationPreABCRigid.inputs.use_estimate_learning_rate_once = [
False
]
A2SantsRegistrationPreABCRigid.inputs.write_composite_transform = True # Required for initialize_transforms_per_stage
A2SantsRegistrationPreABCRigid.inputs.collapse_output_transforms = False # Mutually Exclusive with initialize_transforms_per_stage
A2SantsRegistrationPreABCRigid.inputs.initialize_transforms_per_stage = True
A2SantsRegistrationPreABCRigid.inputs.output_transform_prefix = 'AtlasToSubjectPreBABC_Rigid'
A2SantsRegistrationPreABCRigid.inputs.winsorize_lower_quantile = 0.01
A2SantsRegistrationPreABCRigid.inputs.winsorize_upper_quantile = 0.99
A2SantsRegistrationPreABCRigid.inputs.output_warped_image = 'atlas2subjectRigid.nii.gz'
A2SantsRegistrationPreABCRigid.inputs.output_inverse_warped_image = 'subject2atlasRigid.nii.gz'
A2SantsRegistrationPreABCRigid.inputs.float = True
tissueClassifyWF.connect(inputsSpec, 'atlasToSubjectInitialTransform',
A2SantsRegistrationPreABCRigid,
'initial_moving_transform')
tissueClassifyWF.connect(inputsSpec, 'PrimaryT1',
A2SantsRegistrationPreABCRigid, 'fixed_image')
tissueClassifyWF.connect(inputsSpec, 'atlasVolume',
A2SantsRegistrationPreABCRigid, 'moving_image')
##### Initialize with ANTS Transform For SyN component BABC
currentAtlasToSubjectantsRegistration = 'AtlasToSubjectANTsPreABC_SyN'
A2SantsRegistrationPreABCSyN = pe.Node(
interface=ants.Registration(),
name=currentAtlasToSubjectantsRegistration)
many_cpu_ANTsSyN_options_dictionary = {
'qsub_args': modify_qsub_args(CLUSTER_QUEUE_LONG, 8, 8, 12),
'overwrite': True
}
A2SantsRegistrationPreABCSyN.plugin_args = many_cpu_ANTsSyN_options_dictionary
A2SantsRegistrationPreABCSyN.inputs.num_threads = -1
A2SantsRegistrationPreABCSyN.inputs.dimension = 3
A2SantsRegistrationPreABCSyN.inputs.transforms = ["SyN", "SyN"]
A2SantsRegistrationPreABCSyN.inputs.transform_parameters = [[0.1, 3, 0],
[0.1, 3, 0]]
A2SantsRegistrationPreABCSyN.inputs.metric = ['CC', 'CC']
A2SantsRegistrationPreABCSyN.inputs.sampling_strategy = [None, None]
A2SantsRegistrationPreABCSyN.inputs.sampling_percentage = [1.0, 1.0]
A2SantsRegistrationPreABCSyN.inputs.metric_weight = [1.0, 1.0]
A2SantsRegistrationPreABCSyN.inputs.radius_or_number_of_bins = [4, 4]
A2SantsRegistrationPreABCSyN.inputs.number_of_iterations = [[500, 500],
[500, 70]]
A2SantsRegistrationPreABCSyN.inputs.convergence_threshold = [1e-8, 1e-6]
A2SantsRegistrationPreABCSyN.inputs.convergence_window_size = [12]
A2SantsRegistrationPreABCSyN.inputs.use_histogram_matching = [True, True]
A2SantsRegistrationPreABCSyN.inputs.shrink_factors = [[8, 4], [2, 1]]
A2SantsRegistrationPreABCSyN.inputs.smoothing_sigmas = [[3, 2], [1, 0]]
A2SantsRegistrationPreABCSyN.inputs.sigma_units = ["vox", "vox"]
A2SantsRegistrationPreABCSyN.inputs.use_estimate_learning_rate_once = [
False, False
]
A2SantsRegistrationPreABCSyN.inputs.write_composite_transform = True # Required for initialize_transforms_per_stage
A2SantsRegistrationPreABCSyN.inputs.collapse_output_transforms = False # Mutually Exclusive with initialize_transforms_per_stage
A2SantsRegistrationPreABCSyN.inputs.initialize_transforms_per_stage = True
A2SantsRegistrationPreABCSyN.inputs.save_state = 'SavedInternalSyNState.h5'
A2SantsRegistrationPreABCSyN.inputs.output_transform_prefix = 'AtlasToSubjectPreBABC_SyN'
A2SantsRegistrationPreABCSyN.inputs.winsorize_lower_quantile = 0.01
A2SantsRegistrationPreABCSyN.inputs.winsorize_upper_quantile = 0.99
A2SantsRegistrationPreABCSyN.inputs.output_warped_image = 'atlas2subject.nii.gz'
A2SantsRegistrationPreABCSyN.inputs.output_inverse_warped_image = 'subject2atlas.nii.gz'
A2SantsRegistrationPreABCSyN.inputs.float = True
## if using Registration masking, then do ROIAuto on fixed and moving images and connect to registraitons
if UseRegistrationMasking == True:
from nipype.interfaces.semtools.segmentation.specialized import BRAINSROIAuto
fixedROIAuto = pe.Node(interface=BRAINSROIAuto(),
name="fixedImageROIAUTOMask")
fixedROIAuto.inputs.ROIAutoDilateSize = 10
fixedROIAuto.inputs.outputROIMaskVolume = "fixedImageROIAutoMask.nii.gz"
movingROIAuto = pe.Node(interface=BRAINSROIAuto(),
name="movingImageROIAUTOMask")
fixedROIAuto.inputs.ROIAutoDilateSize = 10
movingROIAuto.inputs.outputROIMaskVolume = "movingImageROIAutoMask.nii.gz"
tissueClassifyWF.connect(inputsSpec, 'PrimaryT1', fixedROIAuto,
'inputVolume')
tissueClassifyWF.connect(inputsSpec, 'atlasVolume', movingROIAuto,
'inputVolume')
tissueClassifyWF.connect(fixedROIAuto, 'outputROIMaskVolume',
A2SantsRegistrationPreABCRigid,
'fixed_image_mask')
tissueClassifyWF.connect(movingROIAuto, 'outputROIMaskVolume',
A2SantsRegistrationPreABCRigid,
'moving_image_mask')
tissueClassifyWF.connect(fixedROIAuto, 'outputROIMaskVolume',
A2SantsRegistrationPreABCSyN,
'fixed_image_mask')
tissueClassifyWF.connect(movingROIAuto, 'outputROIMaskVolume',
A2SantsRegistrationPreABCSyN,
'moving_image_mask')
tissueClassifyWF.connect(A2SantsRegistrationPreABCRigid,
'composite_transform',
A2SantsRegistrationPreABCSyN,
'initial_moving_transform')
tissueClassifyWF.connect(inputsSpec, 'PrimaryT1',
A2SantsRegistrationPreABCSyN, 'fixed_image')
tissueClassifyWF.connect(inputsSpec, 'atlasVolume',
A2SantsRegistrationPreABCSyN, 'moving_image')
BABCext = pe.Node(interface=BRAINSABCext(), name="BABC")
many_cpu_BABC_options_dictionary = {
'qsub_args': modify_qsub_args(CLUSTER_QUEUE, 8, 2, 4),
'overwrite': True
}
BABCext.plugin_args = many_cpu_BABC_options_dictionary
tissueClassifyWF.connect(makeOutImageList, 'inImageList', BABCext,
'inputVolumes')
tissueClassifyWF.connect(makeOutImageList, 'imageTypeList', BABCext,
'inputVolumeTypes')
tissueClassifyWF.connect(makeOutImageList, 'outImageList', BABCext,
'outputVolumes')
BABCext.inputs.debuglevel = 0
BABCext.inputs.useKNN = True
BABCext.inputs.maxIterations = 3
BABCext.inputs.maxBiasDegree = 4
BABCext.inputs.filterIteration = 3
BABCext.inputs.filterMethod = 'GradientAnisotropicDiffusion'
BABCext.inputs.atlasToSubjectTransformType = 'SyN'
BABCext.inputs.gridSize = [10, 10, 10]
BABCext.inputs.outputFormat = "NIFTI"
BABCext.inputs.outputLabels = "brain_label_seg.nii.gz"
BABCext.inputs.outputDirtyLabels = "volume_label_seg.nii.gz"
BABCext.inputs.posteriorTemplate = "POSTERIOR_%s.nii.gz"
BABCext.inputs.atlasToSubjectTransform = "atlas_to_subject.h5"
# BABCext.inputs.implicitOutputs = ['t1_average_BRAINSABC.nii.gz', 't2_average_BRAINSABC.nii.gz']
BABCext.inputs.interpolationMode = InterpolationMode
BABCext.inputs.outputDir = './'
BABCext.inputs.saveState = 'SavedBABCInternalSyNState.h5'
tissueClassifyWF.connect(inputsSpec, 'atlasDefinition', BABCext,
'atlasDefinition')
# NOTE: MUTUALLY EXCLUSIVE with restoreState
#tissueClassifyWF.connect(A2SantsRegistrationPreABCSyN,
# 'composite_transform',
# BABCext, 'atlasToSubjectInitialTransform')
tissueClassifyWF.connect(A2SantsRegistrationPreABCSyN, 'save_state',
BABCext, 'restoreState')
"""
Get the first T1 and T2 corrected images from BABCext
"""
""" HACK: THIS IS NOT NEEDED! We should use the averged t1 and averaged t2 images instead!
def get_first_T1_and_T2(in_files,T1_count):
'''
Returns the first T1 and T2 file in in_files, based on offset in T1_count.
'''
return in_files[0],in_files[T1_count]
bfc_files = pe.Node(Function(input_names=['in_files','T1_count'],
output_names=['t1_corrected','t2_corrected'],
function=get_first_T1_and_T2), run_without_submitting=True, name='99_bfc_files' )
tissueClassifyWF.connect( inputsSpec, 'T1_count', bfc_files, 'T1_count')
tissueClassifyWF.connect(BABCext,'outputVolumes',bfc_files, 'in_files')
tissueClassifyWF.connect(bfc_files,'t1_corrected',outputsSpec,'t1_corrected')
tissueClassifyWF.connect(bfc_files,'t2_corrected',outputsSpec,'t2_corrected')
#tissueClassifyWF.connect(bfc_files,'pd_corrected',outputsSpec,'pd_corrected')
#tissueClassifyWF.connect(bfc_files,'fl_corrected',outputsSpec,'fl_corrected')
"""
#############
tissueClassifyWF.connect(BABCext, 'saveState', outputsSpec,
'atlasToSubjectRegistrationState')
tissueClassifyWF.connect(BABCext, 'atlasToSubjectTransform', outputsSpec,
'atlasToSubjectTransform')
def MakeInverseTransformFileName(TransformFileName):
"""### HACK: This function is to work around a deficiency in BRAINSABCext where the inverse transform name is not being computed properly
in the list outputs"""
fixed_inverse_name = TransformFileName.replace(".h5", "_Inverse.h5")
return [fixed_inverse_name]
tissueClassifyWF.connect([
(BABCext, outputsSpec, [(('atlasToSubjectTransform',
MakeInverseTransformFileName),
"atlasToSubjectInverseTransform")]),
])
tissueClassifyWF.connect(BABCext, 'outputLabels', outputsSpec,
'outputLabels')
tissueClassifyWF.connect(BABCext, 'outputDirtyLabels', outputsSpec,
'outputHeadLabels')
tissueClassifyWF.connect(BABCext, 'outputT1AverageImage', outputsSpec,
't1_average')
tissueClassifyWF.connect(BABCext, 'outputT2AverageImage', outputsSpec,
't2_average')
tissueClassifyWF.connect(BABCext, 'outputPDAverageImage', outputsSpec,
'pd_average')
tissueClassifyWF.connect(BABCext, 'outputFLAverageImage', outputsSpec,
'fl_average')
## remove tissueClassifyWF.connect( [ ( BABCext, outputsSpec, [ (( 'outputAverageImages', getListIndexOrNoneIfOutOfRange, 0 ), "t1_average")] ), ] )
## remove tissueClassifyWF.connect( [ ( BABCext, outputsSpec, [ (( 'outputAverageImages', getListIndexOrNoneIfOutOfRange, 1 ), "t2_average")] ), ] )
## remove tissueClassifyWF.connect( [ ( BABCext, outputsSpec, [ (( 'outputAverageImages', getListIndexOrNoneIfOutOfRange, 2 ), "pd_average")] ), ] )
MakePosteriorDictionaryNode = pe.Node(Function(
function=MakePosteriorDictionaryFunc,
input_names=['posteriorImages'],
output_names=['posteriorDictionary']),
run_without_submitting=True,
name="99_makePosteriorDictionary")
tissueClassifyWF.connect(BABCext, 'posteriorImages',
MakePosteriorDictionaryNode, 'posteriorImages')
tissueClassifyWF.connect(MakePosteriorDictionaryNode,
'posteriorDictionary', outputsSpec,
'posteriorImages')
return tissueClassifyWF
def CreateLandmarkInitializeWorkflow(WFname,
InterpolationMode,
DoReverseInit=False,
debug=False):
landmarkInitializeWF = pe.Workflow(name=WFname)
#############
inputsSpec = pe.Node(interface=IdentityInterface(fields=[
'inputVolume', 'atlasLandmarkFilename', 'atlasWeightFilename',
'LLSModel', 'inputTemplateModel', 'atlasVolume'
]),
run_without_submitting=True,
name='inputspec')
#############
outputsSpec = pe.Node(interface=IdentityInterface(fields=[
'outputLandmarksInACPCAlignedSpace', 'outputResampledVolume',
'outputResampledCroppedVolume', 'outputLandmarksInInputSpace',
'writeBranded2DImage', 'outputTransform', 'outputMRML',
'atlasToSubjectTransform'
]),
run_without_submitting=True,
name='outputspec')
########################################################/
# Run ACPC Detect on first T1 Image - Base Image
########################################################
BCD = pe.Node(interface=BRAINSConstellationDetector(), name="BCD")
## Use program default BCD.inputs.inputTemplateModel = T1ACPCModelFile
# BCD.inputs.outputVolume = "BCD_OUT" + "_ACPC_InPlace.nii.gz" #$# T1AcpcImageList
BCD.inputs.outputTransform = "BCD" + "_Original2ACPC_transform.h5"
BCD.inputs.outputResampledVolume = "BCD" + "_ACPC.nii.gz"
BCD.inputs.outputLandmarksInInputSpace = "BCD" + "_Original.fcsv"
BCD.inputs.outputLandmarksInACPCAlignedSpace = "BCD" + "_ACPC_Landmarks.fcsv"
BCD.inputs.writeBranded2DImage = "BCD" + "_Branded2DQCimage.png"
# BCD.inputs.outputMRML = "BCD" + "_Scene.mrml"
BCD.inputs.interpolationMode = InterpolationMode
BCD.inputs.houghEyeDetectorMode = 1 # Look for dark eyes like on a T1 image, 0=Look for bright eyes like in a T2 image
BCD.inputs.acLowerBound = 80.0 # Chop the data set 80mm below the AC PC point.
# Entries below are of the form:
landmarkInitializeWF.connect(inputsSpec, 'inputVolume', BCD, 'inputVolume')
landmarkInitializeWF.connect(inputsSpec, 'atlasWeightFilename', BCD,
'atlasLandmarkWeights')
landmarkInitializeWF.connect(inputsSpec, 'atlasLandmarkFilename', BCD,
'atlasLandmarks')
landmarkInitializeWF.connect(inputsSpec, 'LLSModel', BCD, 'LLSModel')
landmarkInitializeWF.connect(inputsSpec, 'inputTemplateModel', BCD,
'inputTemplateModel')
landmarkInitializeWF.connect(inputsSpec, 'atlasVolume', BCD, 'atlasVolume')
########################################################
# Run BLI atlas_to_subject
########################################################
BLI = pe.Node(interface=BRAINSLandmarkInitializer(), name="BLI")
BLI.inputs.outputTransformFilename = "landmarkInitializer_atlas_to_subject_transform.h5"
landmarkInitializeWF.connect(inputsSpec, 'atlasWeightFilename', BLI,
'inputWeightFilename')
landmarkInitializeWF.connect(inputsSpec, 'atlasLandmarkFilename', BLI,
'inputMovingLandmarkFilename')
landmarkInitializeWF.connect(BCD, 'outputLandmarksInACPCAlignedSpace', BLI,
'inputFixedLandmarkFilename')
## This is for debugging purposes, and it is not intended for general use.
if DoReverseInit == True:
########################################################
# Run BLI subject_to_atlas
########################################################
BLI2Atlas = pe.Node(interface=BRAINSLandmarkInitializer(),
name="BLI2Atlas")
BLI2Atlas.inputs.outputTransformFilename = "landmarkInitializer_subject_to_atlas_transform.h5"
landmarkInitializeWF.connect(inputsSpec, 'atlasWeightFilename',
BLI2Atlas, 'inputWeightFilename')
landmarkInitializeWF.connect(inputsSpec, 'atlasLandmarkFilename',
BLI2Atlas, 'inputFixedLandmarkFilename')
landmarkInitializeWF.connect(BCD, 'outputLandmarksInInputSpace',
BLI2Atlas, 'inputMovingLandmarkFilename')
Resample2Atlas = pe.Node(interface=BRAINSResample(),
name="Resample2Atlas")
Resample2Atlas.inputs.interpolationMode = "Linear"
Resample2Atlas.inputs.outputVolume = "subject2atlas.nii.gz"
landmarkInitializeWF.connect(inputsSpec, 'inputVolume', Resample2Atlas,
'inputVolume')
landmarkInitializeWF.connect(BLI2Atlas, 'outputTransformFilename',
Resample2Atlas, 'warpTransform')
landmarkInitializeWF.connect(inputsSpec, 'atlasVolume', Resample2Atlas,
'referenceVolume')
if (DoReverseInit == True) and (debug == True):
ResampleFromAtlas = pe.Node(interface=BRAINSResample(),
name="ResampleFromAtlas")
ResampleFromAtlas.inputs.interpolationMode = "Linear"
ResampleFromAtlas.inputs.outputVolume = "atlas2subject.nii.gz"
landmarkInitializeWF.connect(inputsSpec, 'atlasVolume',
ResampleFromAtlas, 'inputVolume')
landmarkInitializeWF.connect(BLI, 'outputTransformFilename',
ResampleFromAtlas, 'warpTransform')
landmarkInitializeWF.connect(BCD, 'outputResampledVolume',
ResampleFromAtlas, 'referenceVolume')
BROIAUTO = pe.Node(interface=BRAINSROIAuto(), name="BROIAuto_cropped")
BROIAUTO.inputs.outputVolume = "Cropped_BCD_ACPC_Aligned.nii.gz"
BROIAUTO.inputs.ROIAutoDilateSize = 10
BROIAUTO.inputs.cropOutput = True
landmarkInitializeWF.connect(BCD, 'outputResampledVolume', BROIAUTO,
'inputVolume')
landmarkInitializeWF.connect(BROIAUTO, 'outputVolume', outputsSpec,
'outputResampledCroppedVolume')
landmarkInitializeWF.connect(BCD, 'outputLandmarksInACPCAlignedSpace',
outputsSpec,
'outputLandmarksInACPCAlignedSpace')
landmarkInitializeWF.connect(BCD, 'outputResampledVolume', outputsSpec,
'outputResampledVolume')
landmarkInitializeWF.connect(BCD, 'outputLandmarksInInputSpace',
outputsSpec, 'outputLandmarksInInputSpace')
landmarkInitializeWF.connect(BCD, 'outputTransform', outputsSpec,
'outputTransform')
landmarkInitializeWF.connect(BCD, 'outputMRML', outputsSpec, 'outputMRML')
landmarkInitializeWF.connect(BCD, 'writeBranded2DImage', outputsSpec,
'writeBranded2DImage')
landmarkInitializeWF.connect(BLI, 'outputTransformFilename', outputsSpec,
'atlasToSubjectTransform')
return landmarkInitializeWF
def create_tissue_classify_workflow(
WFname, master_config, InterpolationMode, UseRegistrationMasking
):
"""
This function...
:param WFname:
:param master_config:
:param InterpolationMode:
:param UseRegistrationMasking:
:return:
"""
from nipype.interfaces import ants
CLUSTER_QUEUE = master_config["queue"]
CLUSTER_QUEUE_LONG = master_config["long_q"]
tissueClassifyWF = pe.Workflow(name=WFname)
inputsSpec = pe.Node(
interface=IdentityInterface(
fields=[
"T1List",
"T2List",
"PDList",
"FLList",
"OTHERList",
"T1_count",
"PrimaryT1",
"atlasDefinition",
"atlasToSubjectInitialTransform",
"atlasVolume",
"atlasheadregion",
]
),
run_without_submitting=True,
name="inputspec",
)
outputsSpec = pe.Node(
interface=IdentityInterface(
fields=[
"atlasToSubjectTransform",
"atlasToSubjectInverseTransform",
"atlasToSubjectRegistrationState",
"outputLabels",
"outputHeadLabels", # ???
# 't1_corrected', 't2_corrected',
"t1_average",
"t2_average",
"pd_average",
"fl_average",
"posteriorImages",
]
),
run_without_submitting=True,
name="outputspec",
)
########################################################
# Run BABCext on Multi-modal images
########################################################
makeOutImageList = pe.Node(
Function(
function=make_out_from_file,
input_names=[
"T1List",
"T2List",
"PDList",
"FLList",
"OTHERList",
"postfix",
"postfixBFC",
"postfixUnwrapped",
"PrimaryT1",
"ListOutType",
],
output_names=[
"inImageList",
"outImageList",
"outBFCImageList",
"outUnwrappedImageList",
"imageTypeList",
],
),
run_without_submitting=True,
name="99_makeOutImageList",
)
tissueClassifyWF.connect(inputsSpec, "T1List", makeOutImageList, "T1List")
tissueClassifyWF.connect(inputsSpec, "T2List", makeOutImageList, "T2List")
tissueClassifyWF.connect(inputsSpec, "PDList", makeOutImageList, "PDList")
tissueClassifyWF.connect(inputsSpec, "FLList", makeOutImageList, "FLList")
tissueClassifyWF.connect(inputsSpec, "OTHERList", makeOutImageList, "OTHERList")
tissueClassifyWF.connect(inputsSpec, "PrimaryT1", makeOutImageList, "PrimaryT1")
makeOutImageList.inputs.ListOutType = False
makeOutImageList.inputs.postfix = "_corrected.nii.gz"
makeOutImageList.inputs.postfixBFC = "_NOT_USED"
makeOutImageList.inputs.postfixUnwrapped = "_NOT_USED"
##### Initialize with ANTS Transform For AffineComponentBABC
currentAtlasToSubjectantsRigidRegistration = "AtlasToSubjectANTsPreABC_Affine"
A2SantsRegistrationPreABCAffine = pe.Node(
interface=ants.Registration(), name=currentAtlasToSubjectantsRigidRegistration
)
many_cpu_ANTsRigid_options_dictionary = {
"qsub_args": modify_qsub_args(CLUSTER_QUEUE, 4, 2, 8),
"overwrite": True,
}
A2SantsRegistrationPreABCAffine.plugin_args = many_cpu_ANTsRigid_options_dictionary
common_ants_registration_settings(
antsRegistrationNode=A2SantsRegistrationPreABCAffine,
registrationTypeDescription="AtlasToSubjectANTsPreABC_Affine",
output_transform_prefix="AtlasToSubjectPreBABC_Rigid",
output_warped_image="atlas2subjectRigid.nii.gz",
output_inverse_warped_image="subject2atlasRigid.nii.gz",
save_state=None,
invert_initial_moving_transform=False,
initial_moving_transform=None,
)
tissueClassifyWF.connect(
inputsSpec,
"atlasToSubjectInitialTransform",
A2SantsRegistrationPreABCAffine,
"initial_moving_transform",
)
tissueClassifyWF.connect(
inputsSpec, "PrimaryT1", A2SantsRegistrationPreABCAffine, "fixed_image"
)
tissueClassifyWF.connect(
inputsSpec, "atlasVolume", A2SantsRegistrationPreABCAffine, "moving_image"
)
##### Initialize with ANTS Transform For SyN component BABC
currentAtlasToSubjectantsRegistration = "AtlasToSubjectANTsPreABC_SyN"
A2SantsRegistrationPreABCSyN = pe.Node(
interface=ants.Registration(), name=currentAtlasToSubjectantsRegistration
)
many_cpu_ANTsSyN_options_dictionary = {
"qsub_args": modify_qsub_args(CLUSTER_QUEUE_LONG, 8, 8, 16),
"overwrite": True,
}
A2SantsRegistrationPreABCSyN.plugin_args = many_cpu_ANTsSyN_options_dictionary
common_ants_registration_settings(
antsRegistrationNode=A2SantsRegistrationPreABCSyN,
registrationTypeDescription="AtlasToSubjectANTsPreABC_SyN",
output_transform_prefix="AtlasToSubjectPreBABC_SyN",
output_warped_image="atlas2subject.nii.gz",
output_inverse_warped_image="subject2atlas.nii.gz",
save_state="SavedInternalSyNState.h5",
invert_initial_moving_transform=False,
initial_moving_transform=None,
)
## if using Registration masking, then do ROIAuto on fixed and moving images and connect to registraitons
if UseRegistrationMasking == True:
from nipype.interfaces.semtools.segmentation.specialized import BRAINSROIAuto
fixedROIAuto = pe.Node(interface=BRAINSROIAuto(), name="fixedImageROIAUTOMask")
fixedROIAuto.inputs.ROIAutoDilateSize = (
15
) ## NOTE Very large to include some skull in bad cases of bias where back of head is very dark
fixedROIAuto.inputs.outputROIMaskVolume = "fixedImageROIAutoMask.nii.gz"
tissueClassifyWF.connect(inputsSpec, "PrimaryT1", fixedROIAuto, "inputVolume")
tissueClassifyWF.connect(
fixedROIAuto,
"outputROIMaskVolume",
A2SantsRegistrationPreABCAffine,
"fixed_image_masks",
)
tissueClassifyWF.connect(
fixedROIAuto,
"outputROIMaskVolume",
A2SantsRegistrationPreABCSyN,
"fixed_image_masks",
)
## NOTE: Always use atlas head region to avoid computing this every time.
tissueClassifyWF.connect(
inputsSpec,
"atlasheadregion",
A2SantsRegistrationPreABCAffine,
"moving_image_masks",
)
tissueClassifyWF.connect(
inputsSpec,
"atlasheadregion",
A2SantsRegistrationPreABCSyN,
"moving_image_masks",
)
tissueClassifyWF.connect(
A2SantsRegistrationPreABCAffine,
"composite_transform",
A2SantsRegistrationPreABCSyN,
"initial_moving_transform",
)
tissueClassifyWF.connect(
inputsSpec, "PrimaryT1", A2SantsRegistrationPreABCSyN, "fixed_image"
)
tissueClassifyWF.connect(
inputsSpec, "atlasVolume", A2SantsRegistrationPreABCSyN, "moving_image"
)
BABCext = pe.Node(interface=BRAINSABCext(), name="BABC")
many_cpu_BABC_options_dictionary = {
"qsub_args": modify_qsub_args(CLUSTER_QUEUE, 13, 8, 16),
"overwrite": True,
}
BABCext.plugin_args = many_cpu_BABC_options_dictionary
tissueClassifyWF.connect(makeOutImageList, "inImageList", BABCext, "inputVolumes")
tissueClassifyWF.connect(
makeOutImageList, "imageTypeList", BABCext, "inputVolumeTypes"
)
tissueClassifyWF.connect(makeOutImageList, "outImageList", BABCext, "outputVolumes")
BABCext.inputs.debuglevel = 0
BABCext.inputs.useKNN = True
BABCext.inputs.purePlugsThreshold = (
0.1
) # New feature to allow for pure plug processing and improvements.
BABCext.inputs.maxIterations = 2
BABCext.inputs.maxBiasDegree = 0
BABCext.inputs.filterIteration = 3
# BABCext.inputs.filterMethod = 'GradientAnisotropicDiffusion' ## If inputs are denoised, we don't need this
BABCext.inputs.filterMethod = "None"
BABCext.inputs.atlasToSubjectTransformType = "SyN"
# Using SyN, so no bsplines here BABCext.inputs.gridSize = [10, 10, 10]
BABCext.inputs.outputFormat = "NIFTI"
BABCext.inputs.outputLabels = "brain_label_seg.nii.gz"
BABCext.inputs.outputDirtyLabels = "volume_label_seg.nii.gz"
BABCext.inputs.posteriorTemplate = "POSTERIOR_%s.nii.gz"
BABCext.inputs.atlasToSubjectTransform = "atlas_to_subject.h5"
# BABCext.inputs.implicitOutputs = ['t1_average_BRAINSABC.nii.gz', 't2_average_BRAINSABC.nii.gz']
BABCext.inputs.interpolationMode = InterpolationMode
BABCext.inputs.outputDir = "./"
BABCext.inputs.saveState = "SavedBABCInternalSyNState.h5"
tissueClassifyWF.connect(inputsSpec, "atlasDefinition", BABCext, "atlasDefinition")
# NOTE: MUTUALLY EXCLUSIVE with restoreState
# tissueClassifyWF.connect(A2SantsRegistrationPreABCSyN,
# 'composite_transform',
# BABCext, 'atlasToSubjectInitialTransform')
tissueClassifyWF.connect(
A2SantsRegistrationPreABCSyN, "save_state", BABCext, "restoreState"
)
"""
Get the first T1 and T2 corrected images from BABCext
"""
""" HACK: THIS IS NOT NEEDED! We should use the averged t1 and averaged t2 images instead!
def get_first_T1_and_T2(in_files,T1_count):
'''
Returns the first T1 and T2 file in in_files, based on offset in T1_count.
'''
return in_files[0],in_files[T1_count]
bfc_files = pe.Node(Function(input_names=['in_files','T1_count'],
output_names=['t1_corrected','t2_corrected'],
function=get_first_T1_and_T2), run_without_submitting=True, name='99_bfc_files' )
tissueClassifyWF.connect( inputsSpec, 'T1_count', bfc_files, 'T1_count')
tissueClassifyWF.connect(BABCext,'outputVolumes',bfc_files, 'in_files')
tissueClassifyWF.connect(bfc_files,'t1_corrected',outputsSpec,'t1_corrected')
tissueClassifyWF.connect(bfc_files,'t2_corrected',outputsSpec,'t2_corrected')
#tissueClassifyWF.connect(bfc_files,'pd_corrected',outputsSpec,'pd_corrected')
#tissueClassifyWF.connect(bfc_files,'fl_corrected',outputsSpec,'fl_corrected')
"""
#############
tissueClassifyWF.connect(
BABCext, "saveState", outputsSpec, "atlasToSubjectRegistrationState"
)
tissueClassifyWF.connect(
BABCext, "atlasToSubjectTransform", outputsSpec, "atlasToSubjectTransform"
)
def make_inverse_transform_filename(TransformFileName):
"""### HACK: This function is to work around a deficiency in BRAINSABCext where the inverse transform name is not being computed properly
in the list outputs
:param Transform:
:return:
"""
fixed_inverse_name = TransformFileName.replace(".h5", "_Inverse.h5")
return [fixed_inverse_name]
tissueClassifyWF.connect(
[
(
BABCext,
outputsSpec,
[
(
("atlasToSubjectTransform", make_inverse_transform_filename),
"atlasToSubjectInverseTransform",
)
],
)
]
)
tissueClassifyWF.connect(BABCext, "outputLabels", outputsSpec, "outputLabels")
tissueClassifyWF.connect(
BABCext, "outputDirtyLabels", outputsSpec, "outputHeadLabels"
)
tissueClassifyWF.connect(BABCext, "outputT1AverageImage", outputsSpec, "t1_average")
tissueClassifyWF.connect(BABCext, "outputT2AverageImage", outputsSpec, "t2_average")
tissueClassifyWF.connect(BABCext, "outputPDAverageImage", outputsSpec, "pd_average")
tissueClassifyWF.connect(BABCext, "outputFLAverageImage", outputsSpec, "fl_average")
## remove tissueClassifyWF.connect( [ ( BABCext, outputsSpec, [ (( 'outputAverageImages', get_list_index_or_none_if_out_of_range, 0 ), "t1_average")] ), ] )
## remove tissueClassifyWF.connect( [ ( BABCext, outputsSpec, [ (( 'outputAverageImages', get_list_index_or_none_if_out_of_range, 1 ), "t2_average")] ), ] )
## remove tissueClassifyWF.connect( [ ( BABCext, outputsSpec, [ (( 'outputAverageImages', get_list_index_or_none_if_out_of_range, 2 ), "pd_average")] ), ] )
MakePosteriorListOfTuplesNode = pe.Node(
Function(
function=make_posteriour_list_of_tuplefunc,
input_names=["posteriorImages"],
output_names=["posteriorDictionary"],
),
run_without_submitting=True,
name="99_makePosteriorDictionary",
)
tissueClassifyWF.connect(
BABCext, "posteriorImages", MakePosteriorListOfTuplesNode, "posteriorImages"
)
tissueClassifyWF.connect(
MakePosteriorListOfTuplesNode,
"posteriorDictionary",
outputsSpec,
"posteriorImages",
)
return tissueClassifyWF
def CreateMALFWorkflow(WFname, onlyT1, master_config,BASE_DATA_GRABBER_DIR=None, runFixFusionLabelMap=True):
from nipype.interfaces import ants
if onlyT1:
n_modality = 1
else:
n_modality = 2
CLUSTER_QUEUE=master_config['queue']
CLUSTER_QUEUE_LONG=master_config['long_q']
MALFWF = pe.Workflow(name=WFname)
inputsSpec = pe.Node(interface=IdentityInterface(fields=['subj_t1_image', #Desired image to create label map for
'subj_t2_image', #Desired image to create label map for
'subj_lmks', #The landmarks corresponding to t1_image
'subj_fixed_head_labels', #The fixed head labels from BABC
'subj_left_hemisphere', #The warped left hemisphere mask
'atlasWeightFilename', #The static weights file name
'labelBaseFilename' #Atlas label base name ex) neuro_lbls.nii.gz
]),
run_without_submitting=True,
name='inputspec')
outputsSpec = pe.Node(interface=IdentityInterface(fields=['MALF_HDAtlas20_2015_label',
'MALF_HDAtlas20_2015_CSFVBInjected_label',
'MALF_HDAtlas20_2015_fs_standard_label',
'MALF_HDAtlas20_2015_lobar_label',
'MALF_extended_snapshot']),
run_without_submitting=True,
name='outputspec')
BLICreator = dict()
A2SantsRegistrationPreMALF_SyN = dict()
fixedROIAuto = dict()
movingROIAuto = dict()
labelMapResample = dict()
NewlabelMapResample = dict()
malf_atlas_mergeindex = 0
merge_input_offset = 1 #Merge nodes are indexed from 1, not zero!
"""
multimodal ants registration if t2 exists
"""
sessionMakeMultimodalInput = pe.Node(Function(function=MakeVector,
input_names=['inFN1', 'inFN2'],
output_names=['outFNs']),
run_without_submitting=True, name="sessionMakeMultimodalInput")
MALFWF.connect(inputsSpec, 'subj_t1_image', sessionMakeMultimodalInput, 'inFN1')
if not onlyT1:
MALFWF.connect(inputsSpec, 'subj_t2_image', sessionMakeMultimodalInput, 'inFN2')
else:
pass
#print('malf_atlas_db_base')
#print(master_config['malf_atlas_db_base'])
malfAtlasDict = readMalfAtlasDbBase( master_config['malf_atlas_db_base'] )
number_of_atlas_sources = len(malfAtlasDict)
malfAtlases = dict()
atlasMakeMultimodalInput = dict()
t2Resample = dict()
warpedAtlasLblMergeNode = pe.Node(interface=Merge(number_of_atlas_sources),name="LblMergeAtlas")
NewwarpedAtlasLblMergeNode = pe.Node(interface=Merge(number_of_atlas_sources),name="fswmLblMergeAtlas")
warpedAtlasesMergeNode = pe.Node(interface=Merge(number_of_atlas_sources*n_modality),name="MergeAtlases")
for malf_atlas_subject in list(malfAtlasDict.keys()):
## Need DataGrabber Here For the Atlas
malfAtlases[malf_atlas_subject] = pe.Node(interface = IdentityInterface(
fields=['t1', 't2', 'label', 'lmks']),
name='malfAtlasInput'+malf_atlas_subject)
malfAtlases[malf_atlas_subject].inputs.t1 = malfAtlasDict[malf_atlas_subject]['t1']
malfAtlases[malf_atlas_subject].inputs.t2 = malfAtlasDict[malf_atlas_subject]['t2']
malfAtlases[malf_atlas_subject].inputs.label = malfAtlasDict[malf_atlas_subject]['label']
malfAtlases[malf_atlas_subject].inputs.lmks = malfAtlasDict[malf_atlas_subject]['lmks']
## Create BLI first
########################################################
# Run BLI atlas_to_subject
########################################################
BLICreator[malf_atlas_subject] = pe.Node(interface=BRAINSLandmarkInitializer(), name="BLI_"+malf_atlas_subject)
BLICreator[malf_atlas_subject].inputs.outputTransformFilename = "landmarkInitializer_{0}_to_subject_transform.h5".format(malf_atlas_subject)
MALFWF.connect(inputsSpec, 'atlasWeightFilename', BLICreator[malf_atlas_subject], 'inputWeightFilename')
MALFWF.connect(malfAtlases[malf_atlas_subject], 'lmks', BLICreator[malf_atlas_subject], 'inputMovingLandmarkFilename')
MALFWF.connect(inputsSpec, 'subj_lmks', BLICreator[malf_atlas_subject], 'inputFixedLandmarkFilename')
##### Initialize with ANTS Transform For SyN
currentAtlasToSubjectantsRegistration = 'SyN_AtlasToSubjectANTsPreMALF_'+malf_atlas_subject
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject] = pe.Node(interface=ants.Registration(), name=currentAtlasToSubjectantsRegistration)
many_cpu_ANTsSyN_options_dictionary = {'qsub_args': modify_qsub_args(CLUSTER_QUEUE_LONG,4,2,16), 'overwrite': True}
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].plugin_args = many_cpu_ANTsSyN_options_dictionary
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.num_threads = -1
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.dimension = 3
#### DEBUGGIN
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.transforms = ["Affine","Affine","SyN","SyN"]
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.transform_parameters = [[0.1],[0.1],[0.1, 3, 0],[0.1, 3, 0]]
if onlyT1:
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.metric = ['MI','MI','CC','CC']
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.metric_weight = [1.0,1.0,1.0,1.0]
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.sampling_percentage = [.5,.5,1.0,1.0]
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.radius_or_number_of_bins = [32,32,4,4]
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.sampling_strategy = ['Regular','Regular',None,None]
else:
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.metric = ['MI',['MI','MI'],'CC',['CC','CC']]
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.metric_weight = [1.0,[1.0,1.0],1.0,[1.0,1.0]]
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.sampling_percentage = [.5,[.5,0.5],1.0,[1.0,1.0]]
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.radius_or_number_of_bins = [32,[32,32],4,[4,4]]
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.sampling_strategy = ['Regular',['Regular','Regular'],None,[None,None]]
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.number_of_iterations = [[1000,1000,500],[500,500],[500,500],[500,70]]
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.convergence_threshold = [1e-8,1e-6,1e-8,1e-6]
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.convergence_window_size = [12]
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.use_histogram_matching = [True,True,True,True]
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.shrink_factors = [[8, 4, 2],[2, 1],[8, 4],[2, 1]]
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.smoothing_sigmas = [[3, 2, 1],[1, 0],[3, 2],[1, 0]]
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.sigma_units = ["vox","vox","vox","vox"]
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.use_estimate_learning_rate_once = [False,False,False,False]
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.write_composite_transform = True # Required for initialize_transforms_per_stage
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.collapse_output_transforms = False # Mutually Exclusive with initialize_transforms_per_stage
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.initialize_transforms_per_stage = True
## NO NEED FOR THIS A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.save_state = 'SavedInternalSyNState.h5'
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.output_transform_prefix = malf_atlas_subject+'_ToSubjectPreMALF_SyN'
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.winsorize_lower_quantile = 0.01
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.winsorize_upper_quantile = 0.99
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.output_warped_image = malf_atlas_subject + '_2subject.nii.gz'
## NO NEED FOR THIS A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.output_inverse_warped_image = 'subject2atlas.nii.gz'
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject].inputs.float = True
## if using Registration masking, then do ROIAuto on fixed and moving images and connect to registraitons
UseRegistrationMasking = True
if UseRegistrationMasking == True:
from nipype.interfaces.semtools.segmentation.specialized import BRAINSROIAuto
fixedROIAuto[malf_atlas_subject] = pe.Node(interface=BRAINSROIAuto(), name="fixedROIAUTOMask_"+malf_atlas_subject)
fixedROIAuto[malf_atlas_subject].inputs.ROIAutoDilateSize=10
fixedROIAuto[malf_atlas_subject].inputs.outputROIMaskVolume = "fixedImageROIAutoMask.nii.gz"
movingROIAuto[malf_atlas_subject] = pe.Node(interface=BRAINSROIAuto(), name="movingROIAUTOMask_"+malf_atlas_subject)
fixedROIAuto[malf_atlas_subject].inputs.ROIAutoDilateSize=10
movingROIAuto[malf_atlas_subject].inputs.outputROIMaskVolume = "movingImageROIAutoMask.nii.gz"
MALFWF.connect(inputsSpec, 'subj_t1_image',fixedROIAuto[malf_atlas_subject],'inputVolume')
MALFWF.connect(malfAtlases[malf_atlas_subject], 't1', movingROIAuto[malf_atlas_subject],'inputVolume')
MALFWF.connect(fixedROIAuto[malf_atlas_subject], 'outputROIMaskVolume',A2SantsRegistrationPreMALF_SyN[malf_atlas_subject],'fixed_image_mask')
MALFWF.connect(movingROIAuto[malf_atlas_subject], 'outputROIMaskVolume',A2SantsRegistrationPreMALF_SyN[malf_atlas_subject],'moving_image_mask')
MALFWF.connect(BLICreator[malf_atlas_subject],'outputTransformFilename',
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject],'initial_moving_transform')
"""
make multimodal input for atlases
"""
atlasMakeMultimodalInput[malf_atlas_subject] = pe.Node(Function(function=MakeVector, input_names=['inFN1', 'inFN2'], output_names=['outFNs']),
run_without_submitting=True, name="atlasMakeMultimodalInput"+malf_atlas_subject)
MALFWF.connect(malfAtlases[malf_atlas_subject], 't1', atlasMakeMultimodalInput[malf_atlas_subject], 'inFN1')
if not onlyT1:
MALFWF.connect(malfAtlases[malf_atlas_subject], 't2', atlasMakeMultimodalInput[malf_atlas_subject], 'inFN2')
else:
pass
MALFWF.connect(sessionMakeMultimodalInput, 'outFNs',
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject],'fixed_image')
MALFWF.connect(atlasMakeMultimodalInput[malf_atlas_subject], 'outFNs',
A2SantsRegistrationPreMALF_SyN[malf_atlas_subject],'moving_image')
MALFWF.connect(A2SantsRegistrationPreMALF_SyN[malf_atlas_subject],'warped_image',
warpedAtlasesMergeNode,'in'+str(merge_input_offset + malf_atlas_mergeindex*n_modality) )
"""
Original t2 resampling
"""
for modality_index in range(1,n_modality):
t2Resample[malf_atlas_subject] = pe.Node(interface=ants.ApplyTransforms(),name="resampledT2"+malf_atlas_subject)
many_cpu_t2Resample_options_dictionary = {'qsub_args': modify_qsub_args(CLUSTER_QUEUE,1,1,1), 'overwrite': True}
t2Resample[malf_atlas_subject].plugin_args = many_cpu_t2Resample_options_dictionary
t2Resample[malf_atlas_subject].inputs.dimension=3
t2Resample[malf_atlas_subject].inputs.output_image=malf_atlas_subject+'_t2.nii.gz'
t2Resample[malf_atlas_subject].inputs.interpolation='BSpline'
t2Resample[malf_atlas_subject].inputs.default_value=0
t2Resample[malf_atlas_subject].inputs.invert_transform_flags=[False]
MALFWF.connect( A2SantsRegistrationPreMALF_SyN[malf_atlas_subject],'composite_transform',
t2Resample[malf_atlas_subject],'transforms')
MALFWF.connect( inputsSpec, 'subj_t1_image',
t2Resample[malf_atlas_subject],'reference_image')
MALFWF.connect( malfAtlases[malf_atlas_subject], 't2',
t2Resample[malf_atlas_subject],'input_image')
MALFWF.connect(t2Resample[malf_atlas_subject],'output_image',
warpedAtlasesMergeNode,'in'+str(merge_input_offset + malf_atlas_mergeindex*n_modality+modality_index) )
"""
Original labelmap resampling
"""
labelMapResample[malf_atlas_subject] = pe.Node(interface=ants.ApplyTransforms(),name="resampledLabel"+malf_atlas_subject)
many_cpu_labelMapResample_options_dictionary = {'qsub_args': modify_qsub_args(CLUSTER_QUEUE,1,1,1), 'overwrite': True}
labelMapResample[malf_atlas_subject].plugin_args = many_cpu_labelMapResample_options_dictionary
labelMapResample[malf_atlas_subject].inputs.dimension=3
labelMapResample[malf_atlas_subject].inputs.output_image=malf_atlas_subject+'_2_subj_lbl.nii.gz'
labelMapResample[malf_atlas_subject].inputs.interpolation='MultiLabel'
labelMapResample[malf_atlas_subject].inputs.default_value=0
labelMapResample[malf_atlas_subject].inputs.invert_transform_flags=[False]
MALFWF.connect( A2SantsRegistrationPreMALF_SyN[malf_atlas_subject],'composite_transform',
labelMapResample[malf_atlas_subject],'transforms')
MALFWF.connect( inputsSpec, 'subj_t1_image',
labelMapResample[malf_atlas_subject],'reference_image')
MALFWF.connect( malfAtlases[malf_atlas_subject], 'label',
labelMapResample[malf_atlas_subject],'input_image')
MALFWF.connect(labelMapResample[malf_atlas_subject],'output_image',warpedAtlasLblMergeNode,'in'+str(merge_input_offset + malf_atlas_mergeindex) )
### New labelmap resampling
NewlabelMapResample[malf_atlas_subject] = pe.Node(interface=ants.ApplyTransforms(),name="FSWM_WLABEL_"+malf_atlas_subject)
many_cpu_NewlabelMapResample_options_dictionary = {'qsub_args': modify_qsub_args(CLUSTER_QUEUE,1,1,1), 'overwrite': True}
NewlabelMapResample[malf_atlas_subject].plugin_args = many_cpu_NewlabelMapResample_options_dictionary
NewlabelMapResample[malf_atlas_subject].inputs.dimension=3
NewlabelMapResample[malf_atlas_subject].inputs.output_image=malf_atlas_subject+'fswm_2_subj_lbl.nii.gz'
NewlabelMapResample[malf_atlas_subject].inputs.interpolation='MultiLabel'
NewlabelMapResample[malf_atlas_subject].inputs.default_value=0
NewlabelMapResample[malf_atlas_subject].inputs.invert_transform_flags=[False]
MALFWF.connect( A2SantsRegistrationPreMALF_SyN[malf_atlas_subject],'composite_transform',
NewlabelMapResample[malf_atlas_subject],'transforms')
MALFWF.connect( inputsSpec, 'subj_t1_image',
NewlabelMapResample[malf_atlas_subject],'reference_image')
MALFWF.connect( malfAtlases[malf_atlas_subject], 'label',
NewlabelMapResample[malf_atlas_subject],'input_image')
MALFWF.connect(NewlabelMapResample[malf_atlas_subject],'output_image',NewwarpedAtlasLblMergeNode,'in'+str(merge_input_offset + malf_atlas_mergeindex) )
malf_atlas_mergeindex += 1
## Now work on cleaning up the label maps
from .FixLabelMapsTools import FixLabelMapFromNeuromorphemetrics2012
from .FixLabelMapsTools import RecodeLabelMap
### Original NeuroMorphometrica merged fusion
jointFusion = pe.Node(interface=ants.JointFusion(),name="JointFusion")
many_cpu_JointFusion_options_dictionary = {'qsub_args': modify_qsub_args(CLUSTER_QUEUE,8,4,4), 'overwrite': True}
jointFusion.plugin_args = many_cpu_JointFusion_options_dictionary
jointFusion.inputs.dimension=3
jointFusion.inputs.method='Joint[0.1,2]'
jointFusion.inputs.output_label_image='MALF_HDAtlas20_2015_label.nii.gz'
MALFWF.connect(warpedAtlasesMergeNode,'out',jointFusion,'warped_intensity_images')
MALFWF.connect(warpedAtlasLblMergeNode,'out',jointFusion,'warped_label_images')
#MALFWF.connect(inputsSpec, 'subj_t1_image',jointFusion,'target_image')
MALFWF.connect(sessionMakeMultimodalInput, 'outFNs',jointFusion,'target_image')
MALFWF.connect(jointFusion, 'output_label_image', outputsSpec,'MALF_HDAtlas20_2015_label')
if onlyT1:
jointFusion.inputs.modalities=1
else:
jointFusion.inputs.modalities=2
## We need to recode values to ensure that the labels match FreeSurer as close as possible by merging
## some labels together to standard FreeSurfer confenventions (i.e. for WMQL)
RECODE_LABELS_2_Standard_FSWM = [
(15071,47),(15072,47),(15073,47),(15145,1011),(15157,1011),(15161,1011),
(15179,1012),(15141,1014),(15151,1017),(15163,1018),(15165,1019),(15143,1027),
(15191,1028),(15193,1028),(15185,1030),(15201,1030),(15175,1031),(15195,1031),
(15173,1035),(15144,2011),(15156,2011),(15160,2011),(15178,2012),(15140,2014),
(15150,2017),(15162,2018),(15164,2019),(15142,2027),(15190,2028),(15192,2028),
(15184,2030),(15174,2031),(15194,2031),(15172,2035),(15200,2030)]
## def RecodeLabelMap(InputFileName,OutputFileName,RECODE_TABLE):
RecodeToStandardFSWM = pe.Node(Function(function=RecodeLabelMap,
input_names=['InputFileName','OutputFileName','RECODE_TABLE'],
output_names=['OutputFileName']),
name="RecodeToStandardFSWM")
RecodeToStandardFSWM.inputs.RECODE_TABLE = RECODE_LABELS_2_Standard_FSWM
RecodeToStandardFSWM.inputs.OutputFileName = 'MALF_HDAtlas20_2015_fs_standard_label.nii.gz'
MALFWF.connect(RecodeToStandardFSWM,'OutputFileName',outputsSpec,'MALF_HDAtlas20_2015_fs_standard_label')
## MALF_SNAPSHOT_WRITER for Segmented result checking:
# MALF_SNAPSHOT_WRITERNodeName = "MALF_ExtendedMALF_SNAPSHOT_WRITER"
# MALF_SNAPSHOT_WRITER = pe.Node(interface=BRAINSSnapShotWriter(), name=MALF_SNAPSHOT_WRITERNodeName)
# MALF_SNAPSHOT_WRITER.inputs.outputFilename = 'MALF_HDAtlas20_2015_CSFVBInjected_label.png' # output specification
# MALF_SNAPSHOT_WRITER.inputs.inputPlaneDirection = [2, 1, 1, 1, 1, 0, 0]
# MALF_SNAPSHOT_WRITER.inputs.inputSliceToExtractInPhysicalPoint = [-3, -7, -3, 5, 7, 22, -22]
# MALFWF.connect(MALF_SNAPSHOT_WRITER,'outputFilename',outputsSpec,'MALF_extended_snapshot')
if runFixFusionLabelMap:
## post processing of jointfusion
injectSurfaceCSFandVBIntoLabelMap = pe.Node(Function(function=FixLabelMapFromNeuromorphemetrics2012,
input_names=['fusionFN',
'FixedHeadFN',
'LeftHemisphereFN',
'outFN',
'OUT_DICT'],
output_names=['fixedFusionLabelFN']),
name="injectSurfaceCSFandVBIntoLabelMap")
injectSurfaceCSFandVBIntoLabelMap.inputs.outFN = 'MALF_HDAtlas20_2015_CSFVBInjected_label.nii.gz'
FREESURFER_DICT = { 'BRAINSTEM': 16, 'RH_CSF':24, 'LH_CSF':24, 'BLOOD': 15000, 'UNKNOWN': 999,
'CONNECTED': [11,12,13,9,17,26,50,51,52,48,53,58]
}
injectSurfaceCSFandVBIntoLabelMap.inputs.OUT_DICT = FREESURFER_DICT
MALFWF.connect(jointFusion, 'output_label_image', injectSurfaceCSFandVBIntoLabelMap, 'fusionFN')
MALFWF.connect(inputsSpec, 'subj_fixed_head_labels', injectSurfaceCSFandVBIntoLabelMap, 'FixedHeadFN')
MALFWF.connect(inputsSpec, 'subj_left_hemisphere', injectSurfaceCSFandVBIntoLabelMap, 'LeftHemisphereFN')
MALFWF.connect(injectSurfaceCSFandVBIntoLabelMap, 'fixedFusionLabelFN',
RecodeToStandardFSWM,'InputFileName')
MALFWF.connect(injectSurfaceCSFandVBIntoLabelMap,'fixedFusionLabelFN',
outputsSpec,'MALF_HDAtlas20_2015_CSFVBInjected_label')
# MALFWF.connect([(inputsSpec, MALF_SNAPSHOT_WRITER, [( 'subj_t1_image','inputVolumes')]),
# (injectSurfaceCSFandVBIntoLabelMap, MALF_SNAPSHOT_WRITER,
# [('fixedFusionLabelFN', 'inputBinaryVolumes')])
# ])
else:
MALFWF.connect(jointFusion, 'output_label_image',
RecodeToStandardFSWM,'InputFileName')
MALFWF.connect(jointFusion, 'output_label_image',
outputsSpec,'MALF_HDAtlas20_2015_CSFVBInjected_label')
# MALFWF.connect([(inputsSpec, MALF_SNAPSHOT_WRITER, [( 'subj_t1_image','inputVolumes')]),
# (jointFusion, MALF_SNAPSHOT_WRITER,
# [('output_label_image', 'inputBinaryVolumes')])
# ])
## Lobar Pacellation by recoding
if master_config['relabel2lobes_filename'] != None:
#print("Generate relabeled version based on {0}".format(master_config['relabel2lobes_filename']))
RECODE_LABELS_2_LobarPacellation = readRecodingList( master_config['relabel2lobes_filename'] )
RecordToFSLobes = pe.Node(Function(function=RecodeLabelMap,
input_names=['InputFileName','OutputFileName','RECODE_TABLE'],
output_names=['OutputFileName']),
name="RecordToFSLobes")
RecordToFSLobes.inputs.RECODE_TABLE = RECODE_LABELS_2_LobarPacellation
RecordToFSLobes.inputs.OutputFileName = 'MALF_HDAtlas20_2015_lobar_label.nii.gz'
MALFWF.connect(RecodeToStandardFSWM, 'OutputFileName',RecordToFSLobes,'InputFileName')
MALFWF.connect(RecordToFSLobes,'OutputFileName',outputsSpec,'MALF_HDAtlas20_2015_lobar_label')
return MALFWF
