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nipype_ants.py
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nipype_ants.py
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from nipype import Node, Function, Workflow
import nipype.interfaces.ants as ants
from nipype.interfaces.io import DataSink
import shutil
import os
def skullstrip_standard_node():
skullstrip_standard = Node(ants.MultiplyImages(
dimension=3,
first_input='/flywheel/v0/templates/mni_icbm152_nlin_asym_09a/mni_icbm152_t1_tal_nlin_asym_09a.nii',
second_input='/flywheel/v0/templates/mni_icbm152_nlin_asym_09a/mni_icbm152_t1_tal_nlin_asym_09a_mask.nii',
output_product_image='/flywheel/v0/templates/mni_icbm152_nlin_asym_09a/mni_icbm152_t1_tal_nlin_asym_09a_brain.nii'),
name='skullstrip_standard_node')
return(skullstrip_standard)
def register_t1_2_standard_node(metric, metric_weight, transforms,
smoothing_sigmas, shrink_factors, number_of_iterations,
transform_parameters):
reg = Node(ants.Registration(metric=metric,
metric_weight=metric_weight,
transforms=transforms,
smoothing_sigmas=smoothing_sigmas,
shrink_factors=shrink_factors,
number_of_iterations=number_of_iterations,
transform_parameters = transform_parameters,
radius_or_number_of_bins = [32]*3,
output_transform_prefix = "output_",
dimension = 3,
write_composite_transform = True,
collapse_output_transforms = False,
initialize_transforms_per_stage = False,
sampling_strategy = ['Random', 'Random', None],
sampling_percentage = [0.05, 0.05, None],
convergence_threshold = [1.e-8,1.e-9,1.e-10],
convergence_window_size = [20]*3,
sigma_units = ['vox']*3,
output_warped_image = 'output_warped_image.nii.gz'),
name='registration_node')
return(reg)
def make_workflow(input_t1, metric, metric_weight, transforms,
smoothing_sigmas, shrink_factors,
number_of_iterations, transform_parameters, has_skull=True):
standard = '/flywheel/v0/templates/mni_icbm152_nlin_asym_09a/mni_icbm152_t1_tal_nlin_asym_09a.nii'
wf = Workflow(name='register2standard', base_dir='/flywheel/v0/work')
registration_node = register_t1_2_standard_node(metric, metric_weight, transforms,
smoothing_sigmas, shrink_factors,
number_of_iterations, transform_parameters)
registration_node.inputs.moving_image = input_t1
registration_node.inputs.fixed_image = standard
if not has_skull:
skullstrip_node = skullstrip_standard_node()
wf.connect(skullstrip_node, "output_product_image", registration_node, "fixed_image")
else:
registration_node.inputs.fixed_image = standard
return(wf)
def test_pype():
transforms = ['Rigid', 'Affine', 'SyN']
metric = ['Mattes']*3
metric_weight = [.7]*3
smoothing_sigmas = [[1], [1], [0]]
shrink_factors = [[1], [1],[1]]
input_t1 = '/tmp/DICOM_T1w_BIC7T_V0_20200114085537_7_ns.nii.gz'
number_of_iterations = [[100],[50],[10]]
transform_parameters = [(0.1,), (0.1,), (0.1, 3.0, 0.0)]
standard = '/flywheel/v0/templates/mni_icbm152_nlin_asym_09a/mni_icbm152_t1_tal_nlin_asym_09a.nii'
wf = make_workflow(input_t1, metric, metric_weight, transforms,
smoothing_sigmas, shrink_factors, number_of_iterations,
transform_parameters, has_skull=False)
transforms = ['Rigid', 'Affine', 'SyN']
metric = ['Mattes'] * 3
metric_weight = [.7] * 3
smoothing_sigmas = [[1], [1], [0]]
shrink_factors = [[1], [1], [1]]
input_t1 = '/Users/davidparker/Documents/Flywheel/SSE/MyWork/Gears/nipype/DockerIO/DICOM_T1w_BIC7T_V0_20200114085537_7_ns.nii.gz'
number_of_iterations = [[100], [50], [10]]
transform_parameters = [(0.1,), (0.1,), (0.1, 3.0, 0.0)]
standard = input_t1
ari=Node(ants.Registration(metric=metric,
metric_weight=metric_weight,
transforms=transforms,
smoothing_sigmas=smoothing_sigmas,
shrink_factors=shrink_factors,
number_of_iterations=number_of_iterations,
fixed_image = standard,
moving_image = input_t1,
transform_parameters = transform_parameters,
radius_or_number_of_bins = [32]*3,
output_transform_prefix = "output_",
dimension = 3,
write_composite_transform = True,
collapse_output_transforms = False,
initialize_transforms_per_stage = False,
sampling_strategy = ['Random', 'Random', None],
sampling_percentage = [0.05, 0.05, None],
convergence_threshold = [1.e-8,1.e-9,1.e-10],
convergence_window_size = [20]*3,
sigma_units=['vox'] * 3),
name='reg_node')
ari.inputs.output_warped_image = 'my_out.nii'
sink = Node(DataSink(), name='sinker')
# Name of the output folder
sink.inputs.base_directory = '/flywheel/v0/output/sink'
# Create a preprocessing workflow
wf = Workflow(name="preprocWF")
wf.base_dir = '/flywheel/v0/output'
# Connect DataSink with the relevant nodes
wf.connect(ari, 'warped_image', sink, 'T1_Out')
wf.run()
shutil.make_archive('/flywheel/v0/zipped_out_dir','zip','/flywheel/v0/output')
shutil.rmtree('/flywheel/v0/output')
os.mkdir('/flywheel/v0/output')
shutil.move('/flywheel/v0/zipped_out_dir.zip','/flywheel/v0/output/zipped_out_dir.zip')
return(wf)
# ari=ants.Registration(metric=metric,
# metric_weight=metric_weight,
# transforms=transforms,
# smoothing_sigmas=smoothing_sigmas,
# shrink_factors=shrink_factors,
# number_of_iterations=number_of_iterations,
# fixed_image = standard,
# moving_image = input_t1,
# transform_parameters = transform_parameters,
# output_warped_image='/warpeed_out.nii',
# verbose=True,
# radius_or_number_of_bins = [32]*3)