def create_get_deformation_shape_analysis(labels,
reduction_rate,
rigid_iteration=1,
affine_iteration=2,
dkw=10,
dkt='Exact',
okw=[8],
dtp=30,
dsk=0.5,
dcps=5,
dcpp='x',
dfcp='Off',
dmi=200,
dat=0.00005,
dls=20,
ods=[0.5],
ot=["NonOrientedSurfaceMesh"],
atlas_image='none',
name='shape_analysis'):
# Create the workflow
workflow = pe.Workflow(name=name)
workflow.base_output_dir = name
# Create the input input node
input_node = pe.Node(niu.IdentityInterface(
fields=['input_images', 'input_ref', 'input_seg', 'subject_ids']),
name='input_node')
# Create the output node
output_node = pe.Node(niu.IdentityInterface(fields=[
'extracted_meshes', 'out_template_vtk_file', 'out_template_CP_file',
'out_template_MOM_file', 'out_template_vtk_files'
]),
name='output_node')
# Create a sub-workflow for groupwise registration
if atlas_image == 'none':
groupwise = create_atlas(itr_rigid=rigid_iteration,
itr_affine=affine_iteration,
itr_non_lin=0,
verbose=False,
name='groupwise')
workflow.connect(input_node, 'input_images', groupwise,
'input_node.in_files')
workflow.connect(input_node, 'input_ref', groupwise,
'input_node.ref_file')
# Create the workflow to create the meshes in an average space
gw_binary_to_meshes = create_binary_to_meshes(
label=labels, reduction_rate=reduction_rate)
workflow.connect(input_node, 'input_images', gw_binary_to_meshes,
'input_node.input_images')
workflow.connect(input_node, 'input_seg', gw_binary_to_meshes,
'input_node.input_parcellations')
workflow.connect(groupwise, 'output_node.trans_files',
gw_binary_to_meshes, 'input_node.trans_files')
workflow.connect(groupwise, 'output_node.average_image',
gw_binary_to_meshes, 'input_node.ref_file')
workflow.connect(gw_binary_to_meshes, 'output_node.output_meshes',
output_node, 'extracted_meshes')
else:
# Create the workflow to create the meshes in an average space
gw_binary_to_meshes = create_binary_to_meshes(
label=labels, reduction_rate=reduction_rate)
workflow.connect(input_node, 'input_images', gw_binary_to_meshes,
'input_node.input_images')
workflow.connect(input_node, 'input_seg', gw_binary_to_meshes,
'input_node.input_parcellations')
workflow.connect(input_node, 'trans_files', gw_binary_to_meshes,
'input_node.trans_files')
workflow.connect(input_node, 'average_image', gw_binary_to_meshes,
'input_node.ref_file')
workflow.connect(gw_binary_to_meshes, 'output_node.output_meshes',
output_node, 'extracted_meshes')
template_computation = atlas_computation(dkw=dkw,
dkt=dkt,
okw=okw,
dtp=dtp,
dsk=dsk,
dcps=dcps,
dcpp=dcpp,
dfcp=dfcp,
dmi=dmi,
dat=dat,
dls=dls,
ods=ods,
type_xml_file='All',
name='template_computation')
workflow.connect(gw_binary_to_meshes, 'output_node.output_meshes',
template_computation, 'input_node.input_vtk_meshes')
workflow.connect(input_node, 'subject_ids', template_computation,
'input_node.subject_ids')
workflow.connect(template_computation, 'output_node.out_template_vtk_file',
output_node, 'out_template_vtk_file')
workflow.connect(template_computation, 'output_node.out_template_CP_file',
output_node, 'out_template_CP_file')
workflow.connect(template_computation, 'output_node.out_template_MOM_file',
output_node, 'out_template_MOM_file')
workflow.connect(template_computation,
'output_node.out_template_vtk_files', output_node,
'out_template_vtk_files')
return workflow
def create_spatio_temporal_analysis(labels,
reduction_rate,
rigid_iteration=1,
affine_iteration=2,
scan_number=2,
name='spatio_temporal_analysis'):
# Create the workflow
workflow = pe.Workflow(name=name)
workflow.base_output_dir = name
# Create the input input node
input_node = pe.Node(niu.IdentityInterface(fields=[
'input_images', 'input_parcellations', 'input_ref', 'label_indices',
'ages', 'subject_ids', 'xml_dkw', 'xml_dkt', 'xml_dtp', 'xml_dsk',
'xml_dcps', 'xml_dcpp', 'xml_dfcp', 'xml_dmi', 'xml_dat', 'xml_dls',
'xml_ods', 'xml_okw', 'xml_ot'
]),
name='input_node')
# Create the output node
output_node = pe.Node(niu.IdentityInterface(fields=[
'extracted_meshes', 'param_diffeo_file', 'param_object_file',
'out_AgeToOnsetNorm_file', 'out_centroid_mat_file',
'out_init_shape_vtk_file', 'out_vertices_centroid_file',
'transported_res_mom', 'transported_res_vect', 'out_file_CP',
'out_file_MOM'
]),
name='output_node')
# Create a sub-workflow for groupwise registration
groupwise = create_atlas(itr_rigid=rigid_iteration,
itr_affine=affine_iteration,
itr_non_lin=0,
name='groupwise')
workflow.connect(input_node, 'input_images', groupwise,
'input_node.in_files')
workflow.connect(input_node, 'input_ref', groupwise, 'input_node.ref_file')
# Create the workflow to create the meshes in an average space
meshes_workflow = create_binary_to_meshes(label=labels,
reduction_rate=reduction_rate)
workflow.connect(input_node, 'input_images', meshes_workflow,
'input_node.input_images')
workflow.connect(input_node, 'input_parcellations', meshes_workflow,
'input_node.input_parcellations')
workflow.connect(groupwise, 'output_node.trans_files', meshes_workflow,
'input_node.trans_files')
workflow.connect(groupwise, 'output_node.average_image', meshes_workflow,
'input_node.ref_file')
workflow.connect(meshes_workflow, 'output_node.output_meshes', output_node,
'extracted_meshes')
# Create the workflow to generate the required data for the regression
# Done for only one label. Should be doable for a set a label, we would analyse together.
preprocessing_regression = create_spatio_temporal_regression_preprocessing(
label=labels,
scan_number=scan_number,
)
workflow.connect(meshes_workflow, 'output_node.output_meshes',
preprocessing_regression, 'input_node.input_meshes')
workflow.connect(input_node, 'ages', preprocessing_regression,
'input_node.ages')
workflow.connect(input_node, 'subject_ids', preprocessing_regression,
'input_node.subject_ids')
workflow.connect(input_node, 'xml_dkw', preprocessing_regression,
'input_node.xml_dkw')
workflow.connect(input_node, 'xml_dkt', preprocessing_regression,
'input_node.xml_dkt')
workflow.connect(input_node, 'xml_dtp', preprocessing_regression,
'input_node.xml_dtp')
workflow.connect(input_node, 'xml_dsk', preprocessing_regression,
'input_node.xml_dsk')
workflow.connect(input_node, 'xml_dcps', preprocessing_regression,
'input_node.xml_dcps')
workflow.connect(input_node, 'xml_dcpp', preprocessing_regression,
'input_node.xml_dcpp')
workflow.connect(input_node, 'xml_dfcp', preprocessing_regression,
'input_node.xml_dfcp')
workflow.connect(input_node, 'xml_dmi', preprocessing_regression,
'input_node.xml_dmi')
workflow.connect(input_node, 'xml_dat', preprocessing_regression,
'input_node.xml_dat')
workflow.connect(input_node, 'xml_dls', preprocessing_regression,
'input_node.xml_dls')
workflow.connect(input_node, 'xml_ods', preprocessing_regression,
'input_node.xml_ods')
workflow.connect(input_node, 'xml_okw', preprocessing_regression,
'input_node.xml_okw')
workflow.connect(input_node, 'xml_ot', preprocessing_regression,
'input_node.xml_ot')
workflow.connect(preprocessing_regression, 'output_node.out_xmlDiffeo',
output_node, 'param_diffeo_file')
workflow.connect(preprocessing_regression, 'output_node.out_xmlObject',
output_node, 'param_object_file')
workflow.connect(preprocessing_regression,
'output_node.out_AgeToOnsetNorm_file', output_node,
'out_AgeToOnsetNorm_file')
workflow.connect(preprocessing_regression,
'output_node.out_centroid_mat_file', output_node,
'out_centroid_mat_file')
workflow.connect(preprocessing_regression,
'output_node.out_init_shape_vtk_file', output_node,
'out_init_shape_vtk_file')
workflow.connect(preprocessing_regression,
'output_node.out_vertices_centroid_file', output_node,
'out_vertices_centroid_file')
# Create the workflow for the computation of the regression and residual deformations transportation
computation_regression = create_get_shape_distance_from_regression(
scan_number=scan_number)
workflow.connect(meshes_workflow, 'output_node.output_meshes',
computation_regression, 'input_node.input_meshes')
workflow.connect(preprocessing_regression, 'output_node.out_xmlDiffeo',
computation_regression, 'input_node.xmlDiffeo')
workflow.connect(preprocessing_regression, 'output_node.out_xmlObject',
computation_regression, 'input_node.xmlObject')
workflow.connect(preprocessing_regression,
'output_node.out_init_shape_vtk_file',
computation_regression, 'input_node.baseline_vtk_file')
workflow.connect(preprocessing_regression,
'output_node.out_AgeToOnsetNorm_file',
computation_regression,
'input_node.in_AgeToOnsetNorm_file')
workflow.connect(computation_regression, 'output_node.transported_res_mom',
output_node, 'transported_res_mom')
workflow.connect(computation_regression,
'output_node.transported_res_vect', output_node,
'transported_res_vect')
workflow.connect(computation_regression, 'output_node.out_file_CP',
output_node, 'out_file_CP')
workflow.connect(computation_regression, 'output_node.out_file_MOM',
output_node, 'out_file_MOM')
return workflow
def create_symmetric_spatio_temporal_analysis(labels,
reduction_rate,
rigid_iteration=1,
affine_iteration=2,
scan_number=2,
name='spatio_temporal_analysis'
):
# Create the workflow
workflow = pe.Workflow(name=name)
workflow.base_output_dir = name
# Create the input input node
input_node = pe.Node(niu.IdentityInterface(
fields=['input_images',
'input_parcellations',
'input_ref',
'label_indices',
'ages',
'subject_ids',
'xml_dkw',
'xml_dkt',
'xml_dtp',
'xml_dsk',
'xml_dcps',
'xml_dcpp',
'xml_dfcp',
'xml_dmi',
'xml_dat',
'xml_dls',
'xml_ods',
'xml_okw',
'xml_ot']),
name='input_node')
# Create the output node
output_node = pe.Node(niu.IdentityInterface(
fields=['extracted_meshes', 'out_template_vtk_file',
'param_diffeo_file', 'param_object_file', 'out_AgeToOnsetNorm_file',
'out_init_shape_vtk_file', 'out_sym_vtk_files',
'transported_res_mom', 'transported_res_vect', 'out_file_CP', 'out_file_MOM']),
name='output_node')
# COMPUTING THE SYMMETRIC OF THE IMAGES BEFORE COMPUTING THE ATLAS
symmetrisation = create_symmetrical_images()
workflow.connect(input_node, 'input_images', symmetrisation, 'input_node.input_images')
workflow.connect(input_node, 'input_parcellations', symmetrisation, 'input_node.input_parcellations')
# Create a sub-workflow for groupwise registration
groupwise = create_atlas(itr_rigid=rigid_iteration,
itr_affine=affine_iteration,
itr_non_lin=0,
verbose=False,
name='groupwise')
workflow.connect(symmetrisation, 'output_node.LR_images', groupwise, 'input_node.in_files')
workflow.connect(input_node, 'input_ref', groupwise, 'input_node.ref_file')
# Create the workflow to create the meshes in an average space
meshes_workflow = create_binary_to_meshes(label=labels, reduction_rate=reduction_rate)
workflow.connect(symmetrisation, 'output_node.LR_images', meshes_workflow, 'input_node.input_images')
workflow.connect(symmetrisation, 'output_node.LR_parcellations', meshes_workflow, 'input_node.input_parcellations')
workflow.connect(groupwise, 'output_node.trans_files', meshes_workflow, 'input_node.trans_files')
workflow.connect(groupwise, 'output_node.average_image', meshes_workflow, 'input_node.ref_file')
workflow.connect(meshes_workflow, 'output_node.output_meshes',
output_node, 'extracted_meshes')
# order the lists of meshes to be [[L R] [L R] [L R]], and age and subject ID accordingly:
reorder_lists = create_lists_symmetrical_subject()
workflow.connect(meshes_workflow, 'output_node.output_meshes',reorder_lists, 'input_node.input_meshes')
workflow.connect(input_node, 'ages', reorder_lists, 'input_node.ages')
workflow.connect(input_node, 'subject_ids', reorder_lists, 'input_node.subject_ids')
# Create the workflow to generate the required data for the regression
# Done for only one label. Should be doable for a set a label, we would analyse together.
preprocessing_regression = create_spatio_temporal_regression_preprocessing(label=labels[0],
scan_number=scan_number
)
workflow.connect(meshes_workflow, 'output_node.output_meshes',
preprocessing_regression, 'input_node.input_meshes')
workflow.connect(reorder_lists, 'output_node.meshes_sorted_by_suj',
preprocessing_regression, 'input_node.input_meshes_sorted')
workflow.connect(reorder_lists, 'output_node.ages_sorted_by_suj',
preprocessing_regression, 'input_node.ages')
workflow.connect(input_node, 'ages',
preprocessing_regression, 'input_node.ages_per_suj')
workflow.connect(reorder_lists, 'output_node.subject_ids_sorted_by_suj',
preprocessing_regression, 'input_node.subject_ids')
workflow.connect(input_node, 'subject_ids',
preprocessing_regression, 'input_node.subject_ids_by_suj')
workflow.connect(input_node, 'xml_dkw',
preprocessing_regression, 'input_node.xml_dkw')
workflow.connect(input_node, 'xml_dkt',
preprocessing_regression, 'input_node.xml_dkt')
workflow.connect(input_node, 'xml_dtp',
preprocessing_regression, 'input_node.xml_dtp')
workflow.connect(input_node, 'xml_dsk',
preprocessing_regression, 'input_node.xml_dsk')
workflow.connect(input_node, 'xml_dcps',
preprocessing_regression, 'input_node.xml_dcps')
workflow.connect(input_node, 'xml_dcpp',
preprocessing_regression, 'input_node.xml_dcpp')
workflow.connect(input_node, 'xml_dfcp',
preprocessing_regression, 'input_node.xml_dfcp')
workflow.connect(input_node, 'xml_dmi',
preprocessing_regression, 'input_node.xml_dmi')
workflow.connect(input_node, 'xml_dat',
preprocessing_regression, 'input_node.xml_dat')
workflow.connect(input_node, 'xml_dls',
preprocessing_regression, 'input_node.xml_dls')
workflow.connect(input_node, 'xml_ods',
preprocessing_regression, 'input_node.xml_ods')
workflow.connect(input_node, 'xml_okw',
preprocessing_regression, 'input_node.xml_okw')
workflow.connect(input_node, 'xml_ot',
preprocessing_regression, 'input_node.xml_ot')
workflow.connect(preprocessing_regression, 'output_node.out_xmlDiffeo', output_node, 'param_diffeo_file')
workflow.connect(preprocessing_regression, 'output_node.out_xmlObject', output_node, 'param_object_file')
workflow.connect(preprocessing_regression, 'output_node.out_AgeToOnsetNorm_file',
output_node, 'out_AgeToOnsetNorm_file')
workflow.connect(preprocessing_regression, 'output_node.out_init_shape_vtk_file',
output_node, 'out_template_vtk_file')
workflow.connect(preprocessing_regression, 'output_node.out_init_shape_vtk_file',
output_node, 'out_init_shape_vtk_file')
workflow.connect(preprocessing_regression, 'output_node.out_sym_vtk_files',
output_node, 'out_sym_vtk_files')
# workflow.connect(preprocessing_regression, 'output_node.out_vertices_centroid_file',
# output_node, 'out_vertices_centroid_file')
# Create the workflow for the computation of the regression and residual deformations transportation
computation_regression = create_get_shape_distance_from_regression(scan_number=scan_number)
workflow.connect(preprocessing_regression, 'output_node.out_sym_vtk_files',
computation_regression, 'input_node.input_meshes')
workflow.connect(preprocessing_regression, 'output_node.out_xmlDiffeo',
computation_regression, 'input_node.xmlDiffeo')
workflow.connect(preprocessing_regression, 'output_node.out_xmlObject',
computation_regression, 'input_node.xmlObject')
workflow.connect(preprocessing_regression, 'output_node.out_init_shape_vtk_file',
computation_regression, 'input_node.baseline_vtk_file')
workflow.connect(preprocessing_regression, 'output_node.out_AgeToOnsetNorm_file',
computation_regression, 'input_node.in_AgeToOnsetNorm_file')
workflow.connect(computation_regression, 'output_node.transported_res_mom',
output_node, 'transported_res_mom')
workflow.connect(computation_regression, 'output_node.transported_res_vect',
output_node, 'transported_res_vect')
workflow.connect(computation_regression, 'output_node.out_file_CP',
output_node, 'out_file_CP')
workflow.connect(computation_regression, 'output_node.out_file_MOM',
output_node, 'out_file_MOM')
return workflow
def create_preprocessing_shape_analysis_epilepsy_flipping(labels,
reduction_rate,
rigid_iteration=1,
affine_iteration=2,
scan_number=2,
name='spatio_temporal_analysis'
):
# Create the workflow
workflow = pe.Workflow(name=name)
workflow.base_output_dir = name
# Create the input input node
input_node = pe.Node(niu.IdentityInterface(
fields=['input_images',
'input_ref',
'flip_id',
'no_flip_id',
'no_flip_seg',
'flip_seg',
'subject_ids']),
name='input_node')
# Create the output node
output_node = pe.Node(niu.IdentityInterface(
fields=['extracted_meshes']),
name='output_node')
# Extract the sublist of parcelation and T1.
split_list_to_flip_images = pe.Node(interface=Function(function=split_list,
input_names=['including_id', 'all_id', 'list_data'],
output_names='extracted_list'),
name='split_list_to_flip_images')
workflow.connect(input_node, 'flip_id', split_list_to_flip_images, 'including_id')
workflow.connect(input_node, 'subject_ids', split_list_to_flip_images, 'all_id')
workflow.connect(input_node, 'input_images', split_list_to_flip_images, 'list_data')
split_list_to_not_flip_images = pe.Node(interface=Function(function=split_list,
input_names=['including_id', 'all_id', 'list_data'],
output_names='extracted_list'),
name='split_list_to_not_flip_images')
workflow.connect(input_node, 'no_flip_id', split_list_to_not_flip_images, 'including_id')
workflow.connect(input_node, 'subject_ids', split_list_to_not_flip_images, 'all_id')
workflow.connect(input_node, 'input_images', split_list_to_not_flip_images, 'list_data')
split_list_to_flip_seg = pe.Node(interface=Function(function=split_list,
input_names=['including_id', 'all_id', 'list_data'],
output_names='extracted_list'),
name='split_list_to_flip_seg')
workflow.connect(input_node, 'flip_id', split_list_to_flip_seg, 'including_id')
workflow.connect(input_node, 'subject_ids', split_list_to_flip_seg, 'all_id')
workflow.connect(input_node, 'flip_seg', split_list_to_flip_seg, 'list_data')
split_list_to_not_flip_seg = pe.Node(interface=Function(function=split_list,
input_names=['including_id', 'all_id', 'list_data'],
output_names='extracted_list'),
name='split_list_to_not_flip_seg')
workflow.connect(input_node, 'no_flip_id', split_list_to_not_flip_seg, 'including_id')
workflow.connect(input_node, 'subject_ids', split_list_to_not_flip_seg, 'all_id')
workflow.connect(input_node, 'no_flip_seg', split_list_to_not_flip_seg, 'list_data')
# COMPUTING THE SYMMETRIC OF THE IMAGES BEFORE COMPUTING THE ATLAS
swap_images = pe.MapNode(interface=SwapDimImage(), iterfield="image2reorient", name="swap_images")
workflow.connect(split_list_to_flip_images, 'extracted_list', swap_images, 'image2reorient')
swap_images.inputs.axe2flip = "LR"
# create a list of LR swapped parcellations:
swap_parcellations = pe.MapNode(interface=SwapDimImage(), iterfield="image2reorient", name="swap_parcellations")
workflow.connect(split_list_to_flip_seg, 'extracted_list', swap_parcellations, 'image2reorient')
swap_parcellations.inputs.axe2flip = "LR"
# merge the lists
merge_lists_images = pe.Node(interface=niu.Merge(axis='vstack', numinputs=2),
name='merge_lists_images')
workflow.connect(split_list_to_not_flip_images, 'extracted_list', merge_lists_images, 'in1')
workflow.connect(swap_images, 'flipped_image', merge_lists_images, 'in2')
merge_lists_seg = pe.Node(interface=niu.Merge(axis='vstack', numinputs=2),
name='merge_lists_seg')
workflow.connect(split_list_to_not_flip_seg, 'extracted_list', merge_lists_seg, 'in1')
workflow.connect(swap_parcellations, 'flipped_image', merge_lists_seg, 'in2')
# Create a sub-workflow for groupwise registration
groupwise = create_atlas(itr_rigid=rigid_iteration,
itr_affine=affine_iteration,
itr_non_lin=0,
verbose=False,
name='groupwise')
workflow.connect(merge_lists_images, 'out', groupwise, 'input_node.in_files')
workflow.connect(input_node, 'input_ref', groupwise, 'input_node.ref_file')
# Create the workflow to create the meshes in an average space
meshes_workflow = create_binary_to_meshes(label=labels, reduction_rate=reduction_rate)
workflow.connect(merge_lists_images, 'out', meshes_workflow, 'input_node.input_images')
workflow.connect(merge_lists_seg, 'out', meshes_workflow, 'input_node.input_parcellations')
workflow.connect(groupwise, 'output_node.trans_files', meshes_workflow, 'input_node.trans_files')
workflow.connect(groupwise, 'output_node.average_image', meshes_workflow, 'input_node.ref_file')
workflow.connect(meshes_workflow, 'output_node.output_meshes',
output_node, 'extracted_meshes')
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