def _run_interface(self, runtime):
mni_template = os.path.join(os.environ['FSLDIR'], 'data', 'standard',
'MNI152_T1_2mm.nii.gz')
mni_template_mask = os.path.join(os.environ['FSLDIR'], 'data',
'standard',
'MNI152_T1_2mm_brain_mask.nii.gz')
in_file = self.inputs.in_file
mni2input = niftyreg.RegAladin()
mni2input.inputs.verbosity_off_flag = True
mni2input.inputs.ref_file = in_file
mni2input.inputs.flo_file = mni_template
mni2input_res = mni2input.run()
mask_resample = niftyreg.RegResample(inter_val='NN')
if self.inputs.use_nrr:
mni2input_nrr = niftyreg.RegF3D()
mni2input_nrr.inputs.verbosity_off_flag = True
mni2input_nrr.inputs.ref_file = in_file
mni2input_nrr.inputs.flo_file = mni_template
mni2input_nrr.inputs.aff_file = mni2input_res.outputs.aff_file
mni2input_nrr.inputs.vel_flag = True
mni2input_nrr_res = mni2input_nrr.run()
mask_resample.inputs.trans_file = mni2input_nrr_res.outputs.cpp_file
else:
mask_resample.inputs.trans_file = mni2input_res.outputs.aff_file
mask_resample.inputs.ref_file = in_file
mask_resample.inputs.flo_file = mni_template_mask
mask_resample_res = mask_resample.run()
fill_mask = niftyseg.UnaryMaths(operation='fill')
fill_mask.inputs.in_file = mask_resample_res.outputs.out_file
fill_mask.run()
return runtime
def create_mask_from_functional():
workflow = pe.Workflow(name='mask_func')
workflow.base_dir = os.getcwd()
workflow.base_output_dir = 'mask_func'
# Create all the required nodes
input_node = pe.Node(interface=niu.IdentityInterface(fields=['in_files']),
name='input_node')
otsu_filter = pe.MapNode(interface=niftyseg.UnaryMaths(),
name='otsu_filter',
iterfield=['in_file'])
erosion_filter = pe.MapNode(interface=niftyseg.BinaryMathsInteger(),
name='erosion_filter',
iterfield=['in_file'])
lconcomp_filter = pe.MapNode(interface=niftyseg.UnaryMaths(),
name='lconcomp_filter',
iterfield=['in_file'])
dilation_filter = pe.MapNode(interface=niftyseg.BinaryMathsInteger(),
name='dilation_filter',
iterfield=['in_file'])
fill_filter = pe.MapNode(interface=niftyseg.UnaryMaths(),
name='fill_filter',
iterfield=['in_file'])
output_node = pe.Node(interface=niu.IdentityInterface(fields=['mask_files']),
name='output_node')
# Define the node options
otsu_filter.inputs.operation = 'otsu'
erosion_filter.inputs.operation = 'ero'
erosion_filter.inputs.operand_value = 1
lconcomp_filter.inputs.operation = 'lconcomp'
dilation_filter.inputs.operation = 'dil'
dilation_filter.inputs.operand_value = 5
fill_filter.inputs.operation = 'fill'
fill_filter.inputs.output_datatype = 'char'
# Create the connections
workflow.connect(input_node, 'in_files', otsu_filter, 'in_file')
workflow.connect(otsu_filter, 'out_file', erosion_filter, 'in_file')
workflow.connect(erosion_filter, 'out_file', lconcomp_filter, 'in_file')
workflow.connect(lconcomp_filter, 'out_file', dilation_filter, 'in_file')
workflow.connect(dilation_filter, 'out_file', fill_filter, 'in_file')
workflow.connect(fill_filter, 'out_file', output_node, 'mask_files')
return workflow
def create_compute_suvr_pipeline(input_pet,
input_mri,
input_par,
erode_ref,
output_dir,
name='compute_suvr',
norm_region='cereb'):
# Create the workflow
workflow = pe.Workflow(name=name)
workflow.base_dir = output_dir
workflow.base_output_dir = name
# Merge all the parcelation into a binary image
merge_roi = pe.MapNode(interface=niftyseg.UnaryMaths(),
name='merge_roi',
iterfield=['in_file'])
merge_roi.inputs.in_file = input_par
merge_roi.inputs.operation = 'bin'
dilation = pe.MapNode(interface=niftyseg.BinaryMathsInteger(),
name='dilation',
iterfield=['in_file'])
workflow.connect(merge_roi, 'out_file', dilation, 'in_file')
dilation.inputs.operation = 'dil'
dilation.inputs.operand_value = 5
# generate a mask for the pet image
mask_pet = create_mask_from_functional()
mask_pet.inputs.input_node.in_files = input_pet
# The structural image is first register to the pet image
rigid_reg = pe.MapNode(interface=niftyreg.RegAladin(),
name='rigid_reg',
iterfield=['ref_file',
'flo_file',
'rmask_file',
'fmask_file'])
rigid_reg.inputs.rig_only_flag = True
rigid_reg.inputs.verbosity_off_flag = True
rigid_reg.inputs.v_val = 80
rigid_reg.inputs.nosym_flag = False
rigid_reg.inputs.ref_file = input_pet
rigid_reg.inputs.flo_file = input_mri
workflow.connect(mask_pet, 'output_node.mask_files', rigid_reg, 'rmask_file')
workflow.connect(dilation, 'out_file', rigid_reg, 'fmask_file')
# Propagate the ROIs into the pet space
resampling = pe.MapNode(interface=niftyreg.RegResample(),
name='resampling',
iterfield=['ref_file', 'flo_file', 'trans_file'])
resampling.inputs.inter_val = 'NN'
resampling.inputs.verbosity_off_flag = True
resampling.inputs.ref_file = input_pet
resampling.inputs.flo_file = input_par
workflow.connect(rigid_reg, 'aff_file', resampling, 'trans_file')
# The PET image is normalised
normalisation_workflow = create_regional_normalisation_pipeline(erode_ref=erode_ref)
normalisation_workflow.inputs.input_node.input_files = input_pet
workflow.connect(resampling, 'out_file', normalisation_workflow, 'input_node.input_rois')
if norm_region == 'pons':
roi_indices = [35]
elif norm_region == 'gm_cereb':
roi_indices = [39, 40,72,73,74]
elif norm_region == 'wm_subcort':
roi_indices = [45, 46]
else: # full cerebellum
roi_indices = [39, 40, 41, 42, 72, 73, 74]
normalisation_workflow.inputs.input_node.label_indices = roi_indices
# The regional uptake are computed
regional_average_workflow = create_regional_average_pipeline(output_dir=output_dir, neuromorphometrics=True)
workflow.connect(normalisation_workflow, 'output_node.out_files',
regional_average_workflow, 'input_node.in_files')
workflow.connect(resampling, 'out_file',
regional_average_workflow, 'input_node.in_rois')
# Create an output node
output_node = pe.Node(
interface=niu.IdentityInterface(
fields=['norm_files',
'suvr_files',
'tran_files',
'out_par_files']),
name='output_node')
workflow.connect(normalisation_workflow, 'output_node.out_files',
output_node, 'norm_files')
workflow.connect(regional_average_workflow, 'output_node.out_files', output_node, 'suvr_files')
workflow.connect(rigid_reg, 'aff_file', output_node, 'tran_files')
workflow.connect(resampling, 'out_file', output_node, 'out_par_files')
# Create a data sink
ds = pe.Node(nio.DataSink(parameterization=False), name='data_sink')
ds.inputs.base_directory = output_dir
workflow.connect(output_node, 'norm_files', ds, '@norm_files')
workflow.connect(output_node, 'tran_files', ds, '@tran_files')
# Return the created workflow
return workflow
def create_binary_to_meshes(label,
name='gw_binary_to_meshes',
reduction_rate=0.3,
operand_value=1):
# Create the workflow
workflow = pe.Workflow(name=name)
workflow.base_output_dir = name
# Create the input node
input_node = pe.Node(niu.IdentityInterface(fields=[
'input_images', 'input_parcellations', 'input_reference',
'trans_files', 'ref_file'
]),
name='input_node')
# Create the output node
output_node = pe.Node(niu.IdentityInterface(fields=['output_meshes']),
name='output_node')
# Extract the relevant label from the GIF parcellation
extract_label = pe.MapNode(interface=MergeLabels(),
iterfield=['in_file'],
name='extract_label')
extract_label.inputs.roi_list = label
workflow.connect(input_node, 'input_parcellations', extract_label,
'in_file')
# Removing parasite segmentation: Erosion.
erode_binaries = pe.MapNode(interface=niftyseg.BinaryMathsInteger(
operation='ero', operand_value=operand_value),
iterfield=['in_file'],
name='erode_binaries')
workflow.connect(extract_label, 'out_file', erode_binaries, 'in_file')
# Removing parasite segmentation: Dilatation.
dilate_binaries = pe.MapNode(interface=niftyseg.BinaryMathsInteger(
operation='dil', operand_value=operand_value),
iterfield=['in_file'],
name='dilate_binaries')
workflow.connect(erode_binaries, 'out_file', dilate_binaries, 'in_file')
# Apply the relevant transformations to the roi
apply_affine = pe.MapNode(interface=niftyreg.RegResample(inter_val='NN'),
iterfield=['flo_file', 'trans_file'],
name='apply_affine')
workflow.connect(input_node, 'trans_files', apply_affine, 'trans_file')
workflow.connect(input_node, 'ref_file', apply_affine, 'ref_file')
workflow.connect(dilate_binaries, 'out_file', apply_affine, 'flo_file')
# compute the large ROI that correspond to the union of all warped label
extract_union_roi = pe.Node(interface=niftyreg.RegAverage(),
name='extract_union_roi')
workflow.connect(apply_affine, 'out_file', extract_union_roi, 'avg_files')
# Binarise the average ROI
binarise_roi = pe.Node(interface=niftyseg.UnaryMaths(operation='bin'),
name='binarise_roi')
workflow.connect(extract_union_roi, 'out_file', binarise_roi, 'in_file')
# Dilation of the binarise union ROI
dilate_roi = pe.Node(interface=niftyseg.BinaryMathsInteger(
operation='dil', operand_value=4),
name='dilate_roi')
workflow.connect(binarise_roi, 'out_file', dilate_roi, 'in_file')
# Apply the transformations
apply_rigid_refinement = pe.MapNode(interface=niftyreg.RegAladin(
rig_only_flag=True, ln_val=1),
iterfield=['flo_file', 'in_aff_file'],
name='apply_rigid_refinement')
workflow.connect(input_node, 'input_images', apply_rigid_refinement,
'flo_file')
workflow.connect(input_node, 'ref_file', apply_rigid_refinement,
'ref_file')
workflow.connect(input_node, 'trans_files', apply_rigid_refinement,
'in_aff_file')
workflow.connect(dilate_roi, 'out_file', apply_rigid_refinement,
'rmask_file')
# Extract the mesh corresponding to the label
final_resampling = pe.MapNode(
interface=niftyreg.RegResample(inter_val='NN'),
iterfield=['flo_file', 'trans_file'],
name='final_resampling')
workflow.connect(apply_rigid_refinement, 'aff_file', final_resampling,
'trans_file')
workflow.connect(input_node, 'ref_file', final_resampling, 'ref_file')
workflow.connect(dilate_binaries, 'out_file', final_resampling, 'flo_file')
# Extract the mesh corresponding to the label
extract_mesh = pe.MapNode(
interface=Image2VtkMesh(in_reductionRate=reduction_rate),
iterfield=['in_file'],
name='extract_mesh')
workflow.connect(final_resampling, 'out_file', extract_mesh, 'in_file')
# workflow.connect(apply_rigid_refinement, 'aff_file', extract_mesh, 'matrix_file')
# Create a rename for the average image
groupwise_renamer = pe.Node(interface=niu.Rename(format_string='atlas',
keep_ext=True),
name='groupwise_renamer')
workflow.connect(input_node, 'ref_file', groupwise_renamer, 'in_file')
workflow.connect(extract_mesh, 'out_file', output_node, 'output_meshes')
return workflow
def create_steps_propagation_pipeline(name='steps_propagation',
aligned_templates=False):
workflow = pe.Workflow(name=name)
# Create an input node
input_node = pe.Node(
interface=niu.IdentityInterface(
fields=['in_file',
'database_file']),
name='input_node')
extract_db_info = pe.Node(interface=niu.Function(input_names=['in_db_file'], output_names=['input_template_images',
'input_template_labels'],
function=extract_db_info_function),
name='extract_db_info')
workflow.connect(input_node, 'database_file', extract_db_info, 'in_db_file')
# All the template images are affinely registered to the target image
current_aladin = pe.MapNode(interface=niftyreg.RegAladin(verbosity_off_flag=True),
name='aladin',
iterfield=['flo_file'])
workflow.connect(input_node, 'in_file', current_aladin, 'ref_file')
workflow.connect(extract_db_info, 'input_template_images', current_aladin, 'flo_file')
# Compute the affine TLS if required
current_robust_affine = None
if aligned_templates is True:
current_robust_affine = pe.Node(interface=niftyreg.RegAverage(), name='robust_affine')
workflow.connect(current_aladin, 'aff_file', current_robust_affine, 'avg_lts_files')
current_aff_prop = pe.MapNode(interface=niftyreg.RegResample(verbosity_off_flag=True, inter_val='NN'),
name='resample_aff',
iterfield=['flo_file'])
workflow.connect(current_robust_affine, 'out_file', current_aff_prop, 'trans_file')
else:
current_aff_prop = pe.MapNode(interface=niftyreg.RegResample(verbosity_off_flag=True, inter_val='NN'),
name='resample_aff',
iterfield=['flo_file',
'trans_file'])
workflow.connect(current_aladin, 'aff_file', current_aff_prop, 'trans_file')
workflow.connect(input_node, 'in_file', current_aff_prop, 'ref_file')
workflow.connect(extract_db_info, 'input_template_labels', current_aff_prop, 'flo_file')
# Merge all the affine parcellation into one 4D
current_aff_prop_merge = pe.Node(interface=fsl.Merge(dimension='t'), name='merge_aff_prop')
workflow.connect(current_aff_prop, 'out_file', current_aff_prop_merge, 'in_files')
# Combine all the propagated parcellation into a single image
current_aff_prop_max = pe.Node(interface=MaxImage(dimension='T'), name='max_aff')
workflow.connect(current_aff_prop_merge, 'merged_file', current_aff_prop_max, 'in_file')
# Binarise the obtained mask
current_aff_prop_bin = pe.Node(interface=niftyseg.UnaryMaths(operation='bin'), name='bin_aff')
workflow.connect(current_aff_prop_max, 'out_file', current_aff_prop_bin, 'in_file')
# Dilate the obtained mask
current_aff_prop_dil = pe.Node(interface=niftyseg.BinaryMathsInteger(operation='dil', operand_value=10),
name='dil_aff')
workflow.connect(current_aff_prop_bin, 'out_file', current_aff_prop_dil, 'in_file')
# Fill the obtained mask
current_aff_prop_fill = pe.Node(interface=niftyseg.UnaryMaths(operation='fill'), name='fill_aff')
workflow.connect(current_aff_prop_dil, 'out_file', current_aff_prop_fill, 'in_file')
# Crop the target image to speed up the process
current_crop_target = pe.Node(interface=CropImage(), name='crop_target')
workflow.connect(input_node, 'in_file', current_crop_target, 'in_file')
workflow.connect(current_aff_prop_fill, 'out_file', current_crop_target, 'mask_file')
# Crop the mask image to speed up the process
current_crop_mask = pe.Node(interface=CropImage(), name='crop_mask')
workflow.connect(current_aff_prop_fill, 'out_file', current_crop_mask, 'in_file')
workflow.connect(current_aff_prop_fill, 'out_file', current_crop_mask, 'mask_file')
# Perform all the non-linear registration
if aligned_templates is True:
current_f3d = pe.MapNode(interface=niftyreg.RegF3D(sx_val=-2.5, be_val=0.01, verbosity_off_flag=True),
name='f3d',
iterfield=['flo_file'])
workflow.connect(current_robust_affine, 'out_file', current_f3d, 'aff_file')
else:
current_f3d = pe.MapNode(interface=niftyreg.RegF3D(),
name='f3d',
iterfield=['flo_file',
'aff_file'])
workflow.connect(current_aladin, 'aff_file', current_f3d, 'aff_file')
workflow.connect(current_crop_target, 'out_file', current_f3d, 'ref_file')
workflow.connect(current_crop_mask, 'out_file', current_f3d, 'rmask_file')
workflow.connect(extract_db_info, 'input_template_images', current_f3d, 'flo_file')
# Merge all the non-linear warped images into one 4D
current_f3d_temp_merge = pe.Node(interface=fsl.Merge(dimension='t'), name='merge_f3d_temp')
workflow.connect(current_f3d, 'res_file', current_f3d_temp_merge, 'in_files')
# Propagate the obtained mask
current_f3d_prop = pe.MapNode(interface=niftyreg.RegResample(inter_val='NN', verbosity_off_flag=True),
name='f3d_prop',
iterfield=['flo_file',
'trans_file'])
workflow.connect(current_crop_target, 'out_file', current_f3d_prop, 'ref_file')
workflow.connect(extract_db_info, 'input_template_labels', current_f3d_prop, 'flo_file')
workflow.connect(current_f3d, 'cpp_file', current_f3d_prop, 'trans_file')
# Merge all the non-linear warped labels into one 4D
current_f3d_prop_merge = pe.Node(interface=fsl.Merge(dimension='t'), name='merge_f3d_prop')
workflow.connect(current_f3d_prop, 'out_file', current_f3d_prop_merge, 'in_files')
# Extract the consensus parcellation using steps
current_fusion = pe.Node(interface=niftyseg.STEPS(template_num=15, kernel_size=1.5, mrf_value=0.15),
name='fusion')
workflow.connect(current_crop_target, 'out_file', current_fusion, 'in_file')
workflow.connect(current_f3d_temp_merge, 'merged_file', current_fusion, 'warped_img_file')
workflow.connect(current_f3d_prop_merge, 'merged_file', current_fusion, 'warped_seg_file')
workflow.connect(current_aff_prop_fill, 'out_file', current_fusion, 'mask_file')
# Resample the obtained consensus label into the original image space
current_prop_orig_res = pe.MapNode(interface=niftyreg.RegResample(inter_val='NN', verbosity_off_flag=True),
name='prop_orig_res',
iterfield=['flo_file'])
workflow.connect(input_node, 'in_file', current_prop_orig_res, 'ref_file')
workflow.connect(current_fusion, 'out_file', current_prop_orig_res, 'flo_file')
# Connect the output to the output node
output_node = pe.Node(
interface=niu.IdentityInterface(
fields=['parcellated_file']),
name='output_node')
workflow.connect(current_prop_orig_res, 'out_file', output_node, 'parcellated_file')
return workflow
def create_cross_sectional_tbss_pipeline(in_files,
output_dir,
name='cross_sectional_tbss',
skeleton_threshold=0.2,
design_mat=None,
design_con=None):
workflow = pe.Workflow(name=name)
workflow.base_dir = output_dir
workflow.base_output_dir = name
# Create the dtitk groupwise registration workflow
groupwise_dtitk = create_dtitk_groupwise_workflow(in_files=in_files,
name="dtitk_groupwise",
rig_iteration=3,
aff_iteration=3,
nrr_iteration=6)
# Create the average FA map
mean_fa = pe.Node(interface=dtitk.TVtool(), name="mean_fa")
workflow.connect(groupwise_dtitk, 'output_node.out_template', mean_fa,
'in_file')
mean_fa.inputs.operation = 'fa'
# Register the FMRIB58_FA_1mm.nii.gz atlas to the mean FA map
reg_atlas = pe.Node(interface=niftyreg.RegAladin(), name='reg_atlas')
workflow.connect(mean_fa, 'out_file', reg_atlas, 'ref_file')
reg_atlas.inputs.flo_file = os.path.join(os.environ['FSLDIR'], 'data',
'standard',
'FMRIB58_FA_1mm.nii.gz')
# Apply the transformation to the lower cingulum image
war_atlas = pe.Node(interface=niftyreg.RegResample(), name='war_atlas')
workflow.connect(mean_fa, 'out_file', war_atlas, 'ref_file')
war_atlas.inputs.flo_file = os.path.join(os.environ['FSLDIR'], 'data',
'standard',
'LowerCingulum_1mm.nii.gz')
workflow.connect(reg_atlas, 'aff_file', war_atlas, 'trans_file')
war_atlas.inputs.inter_val = 'LIN'
# Threshold the propagated lower cingulum
thr_atlas = pe.Node(interface=niftyseg.BinaryMaths(), name='thr_atlas')
workflow.connect(war_atlas, 'out_file', thr_atlas, 'in_file')
thr_atlas.inputs.operation = 'thr'
thr_atlas.inputs.operand_value = 0.5
# Binarise the propagated lower cingulum
bin_atlas = pe.Node(interface=niftyseg.UnaryMaths(), name='bin_atlas')
workflow.connect(thr_atlas, 'out_file', bin_atlas, 'in_file')
bin_atlas.inputs.operation = 'bin'
# Create all the individual FA maps
individual_fa = pe.MapNode(interface=dtitk.TVtool(),
name="individual_fa",
iterfield=['in_file'])
workflow.connect(groupwise_dtitk, 'output_node.out_res', individual_fa,
'in_file')
individual_fa.inputs.operation = 'fa'
# Create all the individual MD maps
individual_md = pe.MapNode(interface=dtitk.TVtool(),
name="individual_md",
iterfield=['in_file'])
workflow.connect(groupwise_dtitk, 'output_node.out_res', individual_md,
'in_file')
individual_md.inputs.operation = 'tr'
# Create all the individual RD maps
individual_rd = pe.MapNode(interface=dtitk.TVtool(),
name="individual_rd",
iterfield=['in_file'])
workflow.connect(groupwise_dtitk, 'output_node.out_res', individual_rd,
'in_file')
individual_rd.inputs.operation = 'rd'
# Create all the individual RD maps
individual_ad = pe.MapNode(interface=dtitk.TVtool(),
name="individual_ad",
iterfield=['in_file'])
workflow.connect(groupwise_dtitk, 'output_node.out_res', individual_ad,
'in_file')
individual_ad.inputs.operation = 'ad'
# Combine all the warped FA images into a 4D image
merged_4d_fa = pe.Node(interface=fsl.Merge(), name='merged_4d_fa')
merged_4d_fa.inputs.dimension = 't'
workflow.connect(individual_fa, 'out_file', merged_4d_fa, 'in_files')
# Combine all the warped MD images into a 4D image
merged_4d_md = pe.Node(interface=fsl.Merge(), name='merged_4d_md')
merged_4d_md.inputs.dimension = 't'
workflow.connect(individual_md, 'out_file', merged_4d_md, 'in_files')
# Combine all the warped RD images into a 4D image
merged_4d_rd = pe.Node(interface=fsl.Merge(), name='merged_4d_rd')
merged_4d_rd.inputs.dimension = 't'
workflow.connect(individual_rd, 'out_file', merged_4d_rd, 'in_files')
# Combine all the warped AD images into a 4D image
merged_4d_ad = pe.Node(interface=fsl.Merge(), name='merged_4d_ad')
merged_4d_ad.inputs.dimension = 't'
workflow.connect(individual_ad, 'out_file', merged_4d_ad, 'in_files')
# Threshold the 4D FA image to 0
merged_4d_fa_thresholded = pe.Node(interface=niftyseg.BinaryMaths(),
name='merged_4d_fa_thresholded')
merged_4d_fa_thresholded.inputs.operation = 'thr'
merged_4d_fa_thresholded.inputs.operand_value = 0
workflow.connect(merged_4d_fa, 'merged_file', merged_4d_fa_thresholded,
'in_file')
# Extract the min value from the 4D FA image
minimal_value_across_all_fa = pe.Node(interface=niftyseg.UnaryMaths(),
name='minimal_value_across_all_fa')
minimal_value_across_all_fa.inputs.operation = 'tmin'
workflow.connect(merged_4d_fa_thresholded, 'out_file',
minimal_value_across_all_fa, 'in_file')
# Create the mask image
fa_mask = pe.Node(interface=niftyseg.UnaryMaths(), name='fa_mask')
fa_mask.inputs.operation = 'bin'
fa_mask.inputs.output_datatype = 'char'
workflow.connect(minimal_value_across_all_fa, 'out_file', fa_mask,
'in_file')
# Mask the mean FA image
masked_mean_fa = pe.Node(interface=fsl.ApplyMask(), name='masked_mean_fa')
workflow.connect(mean_fa, 'out_file', masked_mean_fa, 'in_file')
workflow.connect(fa_mask, 'out_file', masked_mean_fa, 'mask_file')
# Create the skeleton image
skeleton = pe.Node(interface=fsl.TractSkeleton(), name='skeleton')
skeleton.inputs.skeleton_file = True
workflow.connect(masked_mean_fa, 'out_file', skeleton, 'in_file')
# Threshold the skeleton image
thresholded_skeleton = pe.Node(interface=niftyseg.BinaryMaths(),
name='thresholded_skeleton')
thresholded_skeleton.inputs.operation = 'thr'
thresholded_skeleton.inputs.operand_value = skeleton_threshold
workflow.connect(skeleton, 'skeleton_file', thresholded_skeleton,
'in_file')
# Binarise the skeleton image
binarised_skeleton = pe.Node(interface=niftyseg.UnaryMaths(),
name='binarised_skeleton')
binarised_skeleton.inputs.operation = 'bin'
workflow.connect(thresholded_skeleton, 'out_file', binarised_skeleton,
'in_file')
# Create skeleton distance map
invert_mask1 = pe.Node(interface=niftyseg.BinaryMaths(),
name='invert_mask1')
invert_mask1.inputs.operation = 'mul'
invert_mask1.inputs.operand_value = -1
workflow.connect(fa_mask, 'out_file', invert_mask1, 'in_file')
invert_mask2 = pe.Node(interface=niftyseg.BinaryMaths(),
name='invert_mask2')
invert_mask2.inputs.operation = 'add'
invert_mask2.inputs.operand_value = 1
workflow.connect(invert_mask1, 'out_file', invert_mask2, 'in_file')
invert_mask3 = pe.Node(interface=niftyseg.BinaryMaths(),
name='invert_mask3')
invert_mask3.inputs.operation = 'add'
workflow.connect(invert_mask2, 'out_file', invert_mask3, 'in_file')
workflow.connect(binarised_skeleton, 'out_file', invert_mask3,
'operand_file')
distance_map = pe.Node(interface=fsl.DistanceMap(), name='distance_map')
workflow.connect(invert_mask3, 'out_file', distance_map, 'in_file')
# Project the FA values onto the skeleton
all_fa_projected = pe.Node(interface=fsl.TractSkeleton(),
name='all_fa_projected')
all_fa_projected.inputs.threshold = skeleton_threshold
all_fa_projected.inputs.project_data = True
workflow.connect(masked_mean_fa, 'out_file', all_fa_projected, 'in_file')
workflow.connect(distance_map, 'distance_map', all_fa_projected,
'distance_map')
workflow.connect(merged_4d_fa, 'merged_file', all_fa_projected,
'data_file')
workflow.connect(bin_atlas, 'out_file', all_fa_projected,
'search_mask_file')
# Project the MD values onto the skeleton
all_md_projected = pe.Node(interface=fsl.TractSkeleton(),
name='all_md_projected')
all_md_projected.inputs.threshold = skeleton_threshold
all_md_projected.inputs.project_data = True
workflow.connect(masked_mean_fa, 'out_file', all_md_projected, 'in_file')
workflow.connect(distance_map, 'distance_map', all_md_projected,
'distance_map')
workflow.connect(merged_4d_fa, 'merged_file', all_md_projected,
'data_file')
workflow.connect(merged_4d_md, 'merged_file', all_md_projected,
'alt_data_file')
workflow.connect(bin_atlas, 'out_file', all_md_projected,
'search_mask_file')
# Project the RD values onto the skeleton
all_rd_projected = pe.Node(interface=fsl.TractSkeleton(),
name='all_rd_projected')
all_rd_projected.inputs.threshold = skeleton_threshold
all_rd_projected.inputs.project_data = True
workflow.connect(masked_mean_fa, 'out_file', all_rd_projected, 'in_file')
workflow.connect(distance_map, 'distance_map', all_rd_projected,
'distance_map')
workflow.connect(merged_4d_fa, 'merged_file', all_rd_projected,
'data_file')
workflow.connect(merged_4d_rd, 'merged_file', all_rd_projected,
'alt_data_file')
workflow.connect(bin_atlas, 'out_file', all_rd_projected,
'search_mask_file')
# Project the RD values onto the skeleton
all_ad_projected = pe.Node(interface=fsl.TractSkeleton(),
name='all_ad_projected')
all_ad_projected.inputs.threshold = skeleton_threshold
all_ad_projected.inputs.project_data = True
workflow.connect(masked_mean_fa, 'out_file', all_ad_projected, 'in_file')
workflow.connect(distance_map, 'distance_map', all_ad_projected,
'distance_map')
workflow.connect(merged_4d_fa, 'merged_file', all_ad_projected,
'data_file')
workflow.connect(merged_4d_ad, 'merged_file', all_ad_projected,
'alt_data_file')
workflow.connect(bin_atlas, 'out_file', all_ad_projected,
'search_mask_file')
# Create an output node
output_node = pe.Node(interface=niu.IdentityInterface(fields=[
'mean_fa', 'all_fa_skeletonised', 'all_md_skeletonised',
'all_rd_skeletonised', 'all_ad_skeletonised', 'skeleton',
'skeleton_bin', 't_contrast_raw_stat', 't_contrast_uncorrected_pvalue',
't_contrast_corrected_pvalue'
]),
name='output_node')
# Connect the workflow to the output node
workflow.connect(masked_mean_fa, 'out_file', output_node, 'mean_fa')
workflow.connect(all_fa_projected, 'projected_data', output_node,
'all_fa_skeletonised')
workflow.connect(all_md_projected, 'projected_data', output_node,
'all_md_skeletonised')
workflow.connect(all_rd_projected, 'projected_data', output_node,
'all_rd_skeletonised')
workflow.connect(all_ad_projected, 'projected_data', output_node,
'all_ad_skeletonised')
workflow.connect(skeleton, 'skeleton_file', output_node, 'skeleton')
workflow.connect(binarised_skeleton, 'out_file', output_node,
'skeleton_bin')
# Run randomise if required and connect its output to the output node
if design_mat is not None and design_con is not None:
randomise = pe.Node(interface=fsl.Randomise(), name='randomise')
randomise.inputs.base_name = 'stats_tbss'
randomise.inputs.tfce2D = True
randomise.inputs.num_perm = 5000
workflow.connect(all_fa_projected, 'projected_data', randomise,
'in_file')
randomise.inputs.design_mat = design_mat
randomise.inputs.design_con = design_con
workflow.connect(binarised_skeleton, 'out_file', randomise, 'mask')
workflow.connect(randomise, 'tstat_files', output_node,
't_contrast_raw_stat')
workflow.connect(randomise, 't_p_files', output_node,
't_contrast_uncorrected_pvalue')
workflow.connect(randomise, 't_corrected_p_files', output_node,
't_contrast_corrected_pvalue')
# Create nodes to rename the outputs
mean_fa_renamer = pe.Node(interface=niu.Rename(
format_string='tbss_mean_fa', keep_ext=True),
name='mean_fa_renamer')
workflow.connect(output_node, 'mean_fa', mean_fa_renamer, 'in_file')
mean_sk_renamer = pe.Node(interface=niu.Rename(
format_string='tbss_mean_fa_skeleton', keep_ext=True),
name='mean_sk_renamer')
workflow.connect(output_node, 'skeleton', mean_sk_renamer, 'in_file')
bin_ske_renamer = pe.Node(interface=niu.Rename(
format_string='tbss_mean_fa_skeleton_mask', keep_ext=True),
name='bin_ske_renamer')
workflow.connect(output_node, 'skeleton_bin', bin_ske_renamer, 'in_file')
fa_skel_renamer = pe.Node(interface=niu.Rename(
format_string='tbss_all_fa_skeletonised', keep_ext=True),
name='fa_skel_renamer')
workflow.connect(output_node, 'all_fa_skeletonised', fa_skel_renamer,
'in_file')
md_skel_renamer = pe.Node(interface=niu.Rename(
format_string='tbss_all_md_skeletonised', keep_ext=True),
name='md_skel_renamer')
workflow.connect(output_node, 'all_md_skeletonised', md_skel_renamer,
'in_file')
rd_skel_renamer = pe.Node(interface=niu.Rename(
format_string='tbss_all_rd_skeletonised', keep_ext=True),
name='rd_skel_renamer')
workflow.connect(output_node, 'all_rd_skeletonised', rd_skel_renamer,
'in_file')
ad_skel_renamer = pe.Node(interface=niu.Rename(
format_string='tbss_all_ad_skeletonised', keep_ext=True),
name='ad_skel_renamer')
workflow.connect(output_node, 'all_ad_skeletonised', ad_skel_renamer,
'in_file')
# Create a data sink
ds = pe.Node(nio.DataSink(parameterization=False), name='data_sink')
ds.inputs.base_directory = os.path.abspath(output_dir)
# Connect the data sink
workflow.connect(mean_fa_renamer, 'out_file', ds, '@mean_fa')
workflow.connect(mean_sk_renamer, 'out_file', ds, '@skel_fa')
workflow.connect(bin_ske_renamer, 'out_file', ds, '@bkel_fa')
workflow.connect(fa_skel_renamer, 'out_file', ds, '@all_fa')
workflow.connect(md_skel_renamer, 'out_file', ds, '@all_md')
workflow.connect(rd_skel_renamer, 'out_file', ds, '@all_rd')
workflow.connect(ad_skel_renamer, 'out_file', ds, '@all_ad')
if design_mat is not None and design_con is not None:
workflow.connect(output_node, 't_contrast_raw_stat', ds,
'@t_contrast_raw_stat')
workflow.connect(output_node, 't_contrast_uncorrected_pvalue', ds,
'@t_contrast_uncorrected_pvalue')
workflow.connect(output_node, 't_contrast_corrected_pvalue', ds,
'@t_contrast_corrected_pvalue')
return workflow
def create_image_to_mesh_workflow(input_images,
input_parcellations,
input_label_id,
result_dir,
rigid_iteration=3,
affine_iteration=3,
reduction_rate=0.1,
name='registrations_init'):
# Create the workflow
workflow = pe.Workflow(name=name)
workflow.base_dir = result_dir
workflow.base_output_dir = name
# Create a sub-workflow for groupwise registration
groupwise = create_atlas(itr_rigid=rigid_iteration,
itr_affine=affine_iteration,
itr_non_lin=0,
name='groupwise')
groupwise.inputs.input_node.in_files = input_images
groupwise.inputs.input_node.ref_file = input_images[0]
# Extract the relevant label from the GIF parcellation
extract_label = pe.MapNode(interface=MergeLabels(),
iterfield=['in_file'],
name='extract_label')
extract_label.iterables = ("roi_list", [[l] for l in input_label_id])
extract_label.inputs.in_file = input_parcellations
# Removing parasite segmentation: Erosion.
erode_binaries = pe.MapNode(interface=niftyseg.BinaryMathsInteger(
operation='ero', operand_value=1),
iterfield=['in_file'],
name='erode_binaries')
workflow.connect(extract_label, 'out_file', erode_binaries, 'in_file')
# Removing parasite segmentation: Dilatation.
dilate_binaries = pe.MapNode(interface=niftyseg.BinaryMathsInteger(
operation='dil', operand_value=1),
iterfield=['in_file'],
name='dilate_binaries')
workflow.connect(erode_binaries, 'out_file', dilate_binaries, 'in_file')
# Apply the relevant transformations to the roi
apply_affine = pe.MapNode(interface=niftyreg.RegResample(inter_val='NN'),
iterfield=['flo_file', 'trans_file'],
name='apply_affine')
workflow.connect(groupwise, 'output_node.trans_files', apply_affine,
'trans_file')
workflow.connect(groupwise, 'output_node.average_image', apply_affine,
'ref_file')
workflow.connect(dilate_binaries, 'out_file', apply_affine, 'flo_file')
# compute the large ROI that correspond to the union of all warped label
extract_union_roi = pe.Node(interface=niftyreg.RegAverage(),
name='extract_union_roi')
workflow.connect(apply_affine, 'res_file', extract_union_roi, 'in_files')
# Binarise the average ROI
binarise_roi = pe.Node(interface=niftyseg.UnaryMaths(operation='bin'),
name='binarise_roi')
workflow.connect(extract_union_roi, 'out_file', binarise_roi, 'in_file')
# Dilation of the binarise union ROI
dilate_roi = pe.Node(interface=niftyseg.BinaryMathsInteger(
operation='dil', operand_value=5),
name='dilate_roi')
workflow.connect(binarise_roi, 'out_file', dilate_roi, 'in_file')
# Apply the transformations
apply_rigid_refinement = pe.MapNode(interface=niftyreg.RegAladin(
rig_only_flag=True, ln_val=1),
iterfield=['flo_file', 'in_aff_file'],
name='apply_rigid_refinement')
apply_rigid_refinement.inputs.flo_file = input_images
workflow.connect(groupwise, 'output_node.average_image',
apply_rigid_refinement, 'ref_file')
workflow.connect(groupwise, 'output_node.trans_files',
apply_rigid_refinement, 'in_aff_file')
workflow.connect(dilate_roi, 'out_file', apply_rigid_refinement,
'rmask_file')
# Extract the mesh corresponding to the label
final_resampling = pe.MapNode(
interface=niftyreg.RegResample(inter_val='NN'),
iterfield=['flo_file', 'trans_file'],
name='final_resampling')
workflow.connect(apply_rigid_refinement, 'aff_file', final_resampling,
'trans_file')
workflow.connect(groupwise, 'output_node.average_image', final_resampling,
'ref_file')
workflow.connect(dilate_binaries, 'out_file', final_resampling, 'flo_file')
# Extract the mesh corresponding to the label
extract_mesh = pe.MapNode(
interface=Image2VtkMesh(in_reductionRate=reduction_rate),
iterfield=['in_file'],
name='extract_mesh')
workflow.connect(final_resampling, 'res_file', extract_mesh, 'in_file')
# workflow.connect(apply_rigid_refinement, 'aff_file', extract_mesh, 'matrix_file')
# Create a rename for the average image
groupwise_renamer = pe.Node(interface=niu.Rename(format_string='atlas',
keep_ext=True),
name='groupwise_renamer')
workflow.connect(groupwise, 'output_node.average_image', groupwise_renamer,
'in_file')
# Create a datasink
ds = pe.Node(nio.DataSink(parameterization=False), name='ds')
ds.inputs.base_directory = result_dir
workflow.connect(groupwise_renamer, 'out_file', ds, '@avg')
workflow.connect(apply_rigid_refinement, 'res_file', ds, '@raf_mask')
workflow.connect(extract_union_roi, 'out_file', ds, '@union_mask')
workflow.connect(dilate_roi, 'out_file', ds, '@dilate_mask')
workflow.connect(extract_mesh, 'out_file', ds, 'mesh_vtk')
return workflow