def _run_interface(self, runtime):
inputnode = niu.IdentityInterface(
fields=['intensity_image', 'mask_image'])
inputnode.iterables = ('intensity_image', self.inputs.intensity_images)
intensity_nii_list = []
for f in self.inputs.intensity_images:
mult = Math()
mult.inputs.input_files = [f, self.inputs.mask_image]
mult.inputs.output_file = os.getcwd() + os.sep + os.path.basename(
os.path.splitext(f)[0]) + "_brain.mnc"
mult.inputs.calc_mul = True
mult.run()
intensity_mnc2nii_sh = mnc2nii_shCommand()
intensity_mnc2nii_sh.inputs.in_file = mult.inputs.output_file
intensity_mnc2nii_sh.run()
intensity_nii_list += [intensity_mnc2nii_sh.inputs.out_file]
mask_mnc2nii_sh = mnc2nii_shCommand()
mask_mnc2nii_sh.inputs.truncate_path = True
mask_mnc2nii_sh.inputs.in_file = self.inputs.mask_image
mask_mnc2nii_sh.run()
seg = Atropos()
seg.inputs.dimension = self.inputs.dimension
seg.inputs.intensity_images = intensity_nii_list
seg.inputs.mask_image = mask_mnc2nii_sh.inputs.out_file
seg.inputs.initialization = self.inputs.initialization
seg.inputs.prior_probability_images = self.inputs.prior_probability_images
seg.inputs.number_of_tissue_classes = self.inputs.number_of_tissue_classes
seg.inputs.prior_weighting = self.inputs.prior_weighting
seg.inputs.prior_probability_threshold = self.inputs.prior_probability_threshold
seg.inputs.likelihood_model = self.inputs.likelihood_model
seg.inputs.mrf_smoothing_factor = self.inputs.mrf_smoothing_factor
seg.inputs.mrf_radius = self.inputs.mrf_radius
seg.inputs.icm_use_synchronous_update = self.inputs.icm_use_synchronous_update
seg.inputs.maximum_number_of_icm_terations = self.inputs.maximum_number_of_icm_terations
seg.inputs.convergence_threshold = self.inputs.convergence_threshold
seg.inputs.posterior_formulation = self.inputs.posterior_formulation
seg.inputs.use_random_seed = self.inputs.use_random_seed
seg.inputs.use_mixture_model_proportions = self.inputs.use_mixture_model_proportions
seg.inputs.out_classified_image_name = self.inputs.out_classified_image_name
seg.inputs.save_posteriors = self.inputs.save_posteriors
seg.inputs.output_posteriors_name_template = self.inputs.output_posteriors_name_template
print(seg.cmdline)
seg.run()
seg.outputs = seg._list_outputs() #seg._outputs()
classified_nii2mnc_sh = nii2mnc2Command()
classified_nii2mnc_sh.inputs.in_file = seg.outputs["classified_image"]
classified_nii2mnc_sh.inputs.truncate_path = True
classified_nii2mnc_sh.run()
self.inputs.classified_image = classified_nii2mnc_sh.inputs.out_file
return (runtime)
def _run_interface(self, runtime):
mnc2nii_sh = pe.Node(interface=mnc2nii_shCommand(), name="mnc2nii_sh")
nii2mnc_sh = pe.Node(interface=nii2mnc_shCommand(), name="nii2mnc_sh")
reg = pe.Node(interface=Registration(), name="registration")
mnc2nii_sh.inputs.in_file = self.inputs.moving_image
mnc2nii_sh.run()
mnc2nii_sh_nodes = []
inputs = [
"fixed_image", "fixed_image_mask", "fixed_image_mask",
"fixed_image_mask", "moving_image", "moving_image_mask",
"moving_image_masks"
]
self_inputs = [
self.inputs.fixed_image, self.inputs.fixed_image_mask,
self.inputs.fixed_image_mask, self.inputs.fixed_image_mask,
self.inputs.moving_image, self.inputs.moving_image_mask,
self.inputs.moving_image_masks
]
reg_inputs = [
reg.inputs.fixed_image, reg.inputs.fixed_image_mask,
reg.inputs.fixed_image_mask, reg.inputs.fixed_image_mask,
reg.inputs.moving_image, reg.inputs.moving_image_mask,
reg.inputs.moving_image_masks
]
for s, r, i in zip(self_inputs, reg_inputs, inputs):
if isdefined(s):
mnc2nii_sh = pe.Node(interface=mnc2nii_shCommand(),
name="mnc2nii_sh_" + i,
in_file=self.inputs.fixed_image)
mnc2nii_sh.run()
r = mnc2nii_sh.out_file
if isdefined(self.inputs.dimension):
reg.inputs.dimension = self.inputs.dimension
if isdefined(self.inputs.save_state):
reg.inputs.save_state = self.inputs.save_state
if isdefined(self.inputs.restore_state):
reg.inputs.restore_state = self.inputs.restore_state
if isdefined(self.inputs.initial_moving_transform):
reg.inputs.initial_moving_tr = self.inputs.initial_moving_tr
if isdefined(self.inputs.invert_initial_moving_transform):
reg.inputs.invert_initial_moving_tr = self.inputs.invert_initial_moving_tr
if isdefined(self.inputs.initial_moving_transform_com):
reg.inputs.initial_moving_transform_com = self.inputs.initial_moving_transform_com
if isdefined(self.inputs.metric_item_trait):
reg.inputs.metric_item_trait = self.inputs.metric_item_trait
if isdefined(self.inputs.metric_stage_trait):
reg.inputs.metric_stage_trait = self.inputs.metric_stage_trait
if isdefined(self.inputs.metric):
reg.inputs.metric = self.inputs.metric
if isdefined(self.inputs.metric_weight_item_trait):
reg.inputs.metric_weight_item_trait = self.inputs.metric_weight_item_trait
if isdefined(self.inputs.metric_weight_stage_trait):
reg.inputs.metric_weight_stage_trait = self.inputs.metric_weight_stage_trait
if isdefined(self.inputs.metric_weight):
reg.inputs.metric_weight = self.inputs.metric_weight
if isdefined(self.inputs.radius_bins_item_trait):
reg.inputs.radius_bins_item_trait = self.inputs.radius_bins_item_trait
if isdefined(self.inputs.radius_bins_stage_trait):
reg.inputs.radius_bins_stage_trait = self.inputs.radius_bins_stage_trait
if isdefined(self.inputs.radius_or_number_of_bins):
reg.inputs.radius_or_number_of_bins = self.inputs.radius_or_number_of_bins
if isdefined(self.inputs.sampling_strategy_item_trait):
reg.inputs.sampling_strategy_item_trait = self.inputs.sampling_strategy_item_trait
if isdefined(self.inputs.sampling_strategy_stage_trait):
reg.inputs.sampling_strategy_stage_trait = self.inputs.sampling_strategy_stage_trait
if isdefined(self.inputs.sampling_strategy):
reg.inputs.sampling_strategy = self.inputs.sampling_strategy
if isdefined(self.inputs.sampling_percentage_item_trait):
reg.inputs.sampling_percentage_item_trait = self.inputs.sampling_percentage_item_trait
if isdefined(self.inputs.sampling_percentage_stage_trait):
reg.inputs.sampling_percentage_stage_trait = self.inputs.sampling_percentage_stage_trait
if isdefined(self.inputs.sampling_percentage):
reg.inputs.sampling_percentage = self.inputs.sampling_percentage
if isdefined(self.inputs.use_estimate_learning_rate_once):
reg.inputs.use_estimate_learning_rate_once = self.inputs.use_estimate_learning_rate_once
if isdefined(self.inputs.use_histogram_matching):
reg.inputs.use_histogram_matching = self.inputs.use_histogram_matching
if isdefined(self.inputs.interpolation):
reg.inputs.interpolation = self.inputs.interpolation
if isdefined(self.inputs.interpolation_parameters):
reg.inputs.interpolation_parameters = self.inputs.interpolation_parameters
if isdefined(self.inputs.write_composite_transform):
reg.inputs.write_composite_transform = self.inputs.write_composite_transform
if isdefined(self.inputs.collapse_output_transforms):
reg.inputs.collapse_output_transforms = self.inputs.collapse_output_transforms
if isdefined(self.inputs.initialize_transforms_per_stage):
reg.inputs.initialize_transforms_per_stage = self.inputs.initialize_transforms_per_stage
if isdefined(self.inputs.float): reg.inputs.float = self.inputs.float
if isdefined(self.inputs.transform_parameters):
reg.inputs.transform_parameters = self.inputs.transform_parameters
if isdefined(self.inputs.restrict_deformation):
reg.inputs.restrict_deformation = self.inputs.restrict_deformation
if isdefined(self.inputs.number_of_iterations):
reg.inputs.number_of_iterations = self.inputs.number_of_iterations
if isdefined(self.inputs.smoothing_sigmas):
reg.inputs.smoothing_sigmas = self.inputs.smoothing_sigmas
if isdefined(self.inputs.sigma_units):
reg.inputs.sigma_units = self.inputs.sigma_units
if isdefined(self.inputs.shrink_factors):
reg.inputs.shrink_factors = self.inputs.shrink_factors
if isdefined(self.inputs.convergence_threshold):
reg.inputs.convergence_threshold = self.inputs.convergence_threshold
if isdefined(self.inputs.convergence_window_size):
reg.inputs.convergence_window_size = self.inputs.convergence_window_size
if isdefined(self.inputs.output_transform_prefix):
reg.inputs.output_transform_prefix = self.inputs.output_transform_prefix
if isdefined(self.inputs.output_warped_image):
reg.inputs.output_warped_image = self.inputs.output_warped_image
if isdefined(self.inputs.output_inverse_warped_image):
reg.inputs.output_inverse_warped_image = self.inputs.output_inverse_warped_image
if isdefined(self.inputs.winsorize_upper_quantile):
reg.inputs.winsorize_upper_quantile = self.inputs.winsorize_upper_quantile
if isdefined(self.inputs.winsorize_lower_quantile):
reg.inputs.winsorize_lower_quantile = self.inputs.winsorize_lower_quantile
if isdefined(self.inputs.verbose):
reg.inputs.verbose = self.inputs.verbose
reg.run()
nii2mnc_sh.inputs.in_file = reg.inputs.warped_image
nii2mnc_sh.run()
self.outputs.warped_image = nii2mnc_sh.inputs.warped_image
return (runtime)