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armaneshaghiarmaneshaghi
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New interface for antsCorticalThickness.sh script
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‎nipype/interfaces/ants/segmentation.py

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@@ -290,3 +290,220 @@ def _list_outputs(self):
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outputs = self._outputs().get()
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outputs['output_image'] = os.path.abspath(self._gen_filename('output_image'))
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return outputs
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class antsCorticalThicknessInputSpec(ANTSCommandInputSpec):
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dimension=traits.Enum(3, 2, argstr= '-d %d', usedefault=True,
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desc='image dimension (2 or 3)')
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anatomical_image=File(exists=True,
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argstr='-a %s',
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desc='Structural *intensity* image, typically T1.'
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'If more than one anatomical image is specified,'
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'subsequently specified images are used during the'
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'segmentation process. However, only the first'
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'image is used in the registration of priors.'
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'Our suggestion would be to specify the T1'
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'as the first image.',
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mandatory=True)
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brain_template=File(exists=True,
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argstr='-e %s',
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desc='Anatomical *intensity* template (possibly created using a'
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'population data set with buildtemplateparallel.sh in ANTs).'
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'This template is *not* skull-stripped.')
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brain_probability_mask=File(exists=True,
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argstr='-m %s', desc='brain probability mask in template space',
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mandatory=True, copyfile=False)
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segmentation_priors = InputMultiPath(File(exists=True),
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argstr='-p %s')
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out_prefix = traits.Str('antsCT_', argstr='-o %s', usedefault=True,
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desc=('Prefix that is prepended to all output'
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' files (default = antsCT_)'))
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image_suffix=traits.Str('nii.gz', desc=('any of standard ITK formats,'
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' nii.gz is default'), mandatory = False, argstr='-s %s',
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usedefault=True)
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t1_registration_template = File(exists=True,
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desc = ('Anatomical *intensity* template'
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'(assumed to be skull-stripped). A common'
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'case would be where this would be the same'
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'template as specified in the -e option which'
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'is not skull stripped.'),
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mandatory=True, argstr='-t %s')
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extraction_registration_mask=File(exists=True, argstr='-f %s',
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mandatory=False, desc='Mask (defined in the template'
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'space) used during registration for brain extraction.')
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keep_temporary_files=traits.Int(argstrr='-k %d',
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desc='Keep brain extraction/segmentation'
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'warps, etc (default = 0).', mandatory=False)
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max_iterations=traits.Int(argstr='-i %d',
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desc='ANTS registration max iterations'
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'(default = 100x100x70x20)', mandatory=False)
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prior_segmentation_weight=traits.Float(mandatory=False, argstr='-w %f',
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desc='Atropos spatial prior *probability* weight for'
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'the segmentation')
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segmentation_iterations=traits.Int(argstr='-n %d', mandatory=False,
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desc='N4 -> Atropos -> N4 iterations during segmentation'
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'(default = 3)')
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posterior_formulation=traits.Str(argstr='-b %s', mandatory=False,
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desc=('Atropos posterior formulation and whether or not'
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'to use mixture model proportions.'
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'''e.g 'Socrates[1]' (default) or 'Aristotle[1]'.'''
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'Choose the latter if you'
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'want use the distance priors (see also the -l option'
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'for label propagation control).'))
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use_floatingpoint_precision=traits.Enum(0, 1, argstr='-j %d',
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mandatory=False,
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desc='Use floating point precision in registrations (default = 0)')
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use_random_seeding=traits.Enum(0, 1, argstr='-u %d', mandatory=False,
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desc='Use random number generated from system clock in Atropos'
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'(default = 1)')
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b_spline_smoothing=traits.Bool(argst='-v', mandatory=False,
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desc='Use B-spline SyN for registrations and B-spline'
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'exponential mapping in DiReCT.')
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cortical_label_image=File(exists=True, mandatory=False,
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desc='Cortical ROI labels to use as a prior for ATITH.')
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label_propagation=traits.Str(argstr='-l %s', mandatory=False,
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desc='Incorporate a distance prior one the posterior formulation. Should be'
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'''of the form 'label[lambda,boundaryProbability]' where label'''
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'is a value of 1,2,3,... denoting label ID. The label'
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'probability for anything outside the current label'
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' = boundaryProbability * exp( -lambda * distanceFromBoundary )'
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'Intuitively, smaller lambda values will increase the spatial capture'
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'range of the distance prior. To apply to all label values, simply omit'
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'specifying the label, i.e. -l [lambda,boundaryProbability].')
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quick_registration=traits.Bool(argstr='-q 1', mandatory=False,
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desc='If = 1, use antsRegistrationSyNQuick.sh as the basis for registration'
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'during brain extraction, brain segmentation, and'
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'(optional) normalization to a template.'
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'Otherwise use antsRegistrationSyN.sh (default = 0).')
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debug=traits.Bool(argstr='-z 1', mandatory=False,
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desc='If > 0, runs a faster version of the script.'
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'Only for testing. Implies -u 0.'
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'Requires single thread computation for complete reproducibility.')
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class antsCorticalThicknessoutputSpec(TraitedSpec):
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BrainExtractionMask=File(exists=True,
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desc= 'brain extraction mask')
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BrainSegmentation=File(exists=True,
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desc='brain segmentaion image')
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BrainSegmentationN4=File(exists=True,
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desc='N4 corrected image')
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BrainSegmentationPosteriorsCSF=File(exists=True,
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desc='CSF posterior probability image')
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BrainSegmentationPosteriorsGM=File(exists=True,
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desc='GM posterior probability image')
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BrainSegmentationPosteriorsWM=File(exists=True,
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desc='WM posterior probability image')
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BrainSegmentationPosteriorsDGM=File(exists=True,
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desc='DGM posterior probability image')
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CorticalThickness=File(exists=True,
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desc='cortical thickness file')
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TemplateToSubject1GenericAffine=File(exists=True,
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desc='Template to subject affine')
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TemplateToSubject0Warp=File(exists=True,
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desc='Template to subject warp')
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SubjectToTemplate1Warp=File(exists=True,
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desc='Template to subject inverse warp')
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SubjectToTemplate0GenericAffine=File(exists=True,
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desc='Template to subject inverse affine')
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TemplateToSubjectLogJacobian=File(exists=True,
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desc='Template to subject log jacobian')
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class antsCorticalThickness(ANTSCommand):
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"""
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Examples
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--------
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>>> from nipype.interfaces.ants.segmentation import antsCorticalThickness
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>>> corticalthickness = anstCorticalThickness()
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>>> corticalthickness.inputs.dimension = 3
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>>> corticalthickness.inputs.anatomical_image ='T1.nii.gz'
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>>> corticalthickness.inputs.brain_template = 'study_template.nii.gz'
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>>> corticalthickness.inputs.brain_probability_mask ='ProbabilityMaskOfStudyTemplate.nii.gz'
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>>> corticalthickness.inputs.segmentation_priors =['CSF.nii.gz','WM.nii.gz','GM.nii.gz','DEEP_GM.nii.gz']
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>>> corticalthickness.inputs.t1_registration_template = 'brain_study_template.nii.gz'
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>>> corticalthickness.cmdline
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'antsCorticalThickness.sh -d 3 -a T1.nii.gz -m ProbabilityMaskOfStudyTemplate.nii.gz -e study_template.nii.gz -o antsCT_ -p BrainSegmentationPrior%0d.nii.gz -t brain_study_template.nii.gz'
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"""
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input_spec = antsCorticalThicknessInputSpec
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output_spec = antsCorticalThicknessoutputSpec
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_cmd = 'antsCorticalThickness.sh'
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def _format_arg(self, opt, spec, val):
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if opt == 'anatomical_image':
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retval = '-a %s' %(val)
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return retval
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if opt == 'brain_template':
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retval = '-e %s' %(val)
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return retval
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if opt == 'brain_probability_mask':
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retval = '-m %s' %(val)
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return retval
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if opt == 'out_prefix':
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retval = '-o %s' %(val)
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return retval
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if opt == 't1_registration_template':
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retval = '-t %s' %(val)
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return retval
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if opt == 'segmentation_priors':
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_, _, ext = split_filename(self.inputs.segmentation_priors[0])
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retval = "-p priors/BrainSegmentationPrior%02d"
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retval += ext
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return retval
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return super(ANTSCommand, self)._format_arg(opt, spec, val)
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def _run_interface(self, runtime):
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priors_directory = os.path.join(os.getcwd(), "priors")
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if not os.path.exists(priors_directory):
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os.makedirs(priors_directory)
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_, _, ext = split_filename(self.inputs.segmentation_priors[0])
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for i, f in enumerate(self.inputs.segmentation_priors):
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target = os.path.join(priors_directory,
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'BrainSegmentationPrior%02d' % (i + 1) + ext)
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if not (os.path.exists(target) and os.path.realpath(target) == os.path.abspath(f)):
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copyfile(os.path.abspath(f), os.path.join(priors_directory,
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'BrainSegmentationPrior%02d' % (i + 1) + ext))
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runtime = super(antsCorticalThickness, self)._run_interface(runtime)
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return runtime
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def _list_outputs(self):
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outputs = self._outputs().get()
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outputs['BrainExtractionMask'] = os.path.join(os.getcwd(),
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self.inputs.out_prefix +
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'BrainExtractionMask.'+
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self.inputs.image_suffix)
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outputs['BrainSegmentation'] = os.path.join(os.getcwd(),
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self.inputs.out_prefix +
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'BrainSegmentation.' +
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self.inputs.image_suffix)
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outputs['BrainSegmentationN4'] = os.path.join(os.getcwd(),
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self.inputs.out_prefix +
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'BrainSegmentation0N4.' +
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self.inputs.image_suffix)
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outputs['BrainSegmentationPosteriorsCSF'] = os.path.join(os.getcwd(),
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self.inputs.out_prefix +
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'BrainSegmentationPosteriors01.' +
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self.inputs.image_suffix)
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outputs['BrainSegmentationPosteriorsGM'] = os.path.join(os.getcwd(),
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'BrainSegmentationPosteriors02.' +
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self.inputs.image_suffix)
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outputs['BrainSegmentationPosteriorsWM'] = os.path.join(os.getcwd(),
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'BrainSegmentationPosteriors03.' +
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self.inputs.image_suffix)
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outputs['BrainSegmentationPosteriorsDGM'] = os.path.join(os.getcwd(),
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'BrainSegmentationPosteriors04.' +
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self.inputs.image_suffix)
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outputs['CorticalThickness'] = os.path.join(os.getcwd(),
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self.inputs.out_prefix + 'CorticalThickness.' +
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self.inputs.image_suffix)
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outputs['TemplateToSubject1GenericAffine'] = os.path.join(
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os.getcwd(),
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'TemplateToSubject1GenericAffine.mat')
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outputs['TemplateToSubject0Warp'] = os.path.join(os.getcwd(),
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self.inputs.out_prefix + 'TemplateToSubject0Warp.'+
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self.inputs.image_suffix)
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outputs['SubjectToTemplate1Warp'] = os.path.join(os.getcwd(),
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self.inputs.out_prefix + 'SubjectToTemplate1Warp' +
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self.inputs.image_suffix)
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outputs['SubjectToTemplate0GenericAffine'] = os.path.join(os.getcwd(),
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self.inputs.out_prefix + 'SubjectToTemplate0GenericAffine.mat')
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outputs['TemplateToSubjectLogJacobian'] = os.path.join(os.getcwd(),
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self.inputs.out_prefix + 'subjectToTemplateLogJacobian.'+
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self.inputs.image_suffix)
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return outputs

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