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)
