Merge remote-tracking branch 'upstream/master' into enh/restingconn

* upstream/master:
  ENH: Improve the SGE job names
  fix
  fixed bug
  remove duplicated input
  [Freesurfer] Improved Tkregister2
  multiline description changed to tuple
  sty: white space
  fix: traits specifications for antsCT
  tst: revert tests
  antsCorticalThickness template + test files + merged upstream
  new interface for antsCorticalThickness + test files
  antsCorticalThickness interface + example data for testing
  New interface for antsCorticalThickness.sh script

Author: satra

nipype/interfaces/ants/segmentation.py

@@ -334,3 +334,220 @@ def _list_outputs(self):
         if self.inputs.save_bias or isdefined(self.inputs.bias_image):
             outputs['bias_image'] = os.path.abspath(self._gen_filename('bias_image'))
         return outputs
+
+class antsCorticalThicknessInputSpec(ANTSCommandInputSpec):
+    dimension=traits.Enum(3, 2, argstr= '-d %d', usedefault=True,
+                           desc='image dimension (2 or 3)')
+    anatomical_image=File(exists=True,
+                           argstr='-a %s',
+                           desc=('Structural *intensity* image, typically T1.'
+                           'If more than one anatomical image is specified,'
+                           'subsequently specified images are used during the'
+                           'segmentation process. However, only the first'
+                           'image is used in the registration of priors.'
+                           'Our suggestion would be to specify the T1'
+                           'as the first image.'),
+                           mandatory=True)
+    brain_template=File(exists=True,
+            argstr='-e %s',
+            desc=('Anatomical *intensity* template (possibly created using a'
+            'population data set with buildtemplateparallel.sh in ANTs).'
+            'This template is  *not* skull-stripped.'),
+            mandatory=True)
+    brain_probability_mask=File(exists=True,
+            argstr='-m %s', desc='brain probability mask in template space',
+            copyfile=False, mandatory=True)
+    segmentation_priors = InputMultiPath(File(exists=True),
+            argstr='-p %s', mandatory=True)
+    out_prefix = traits.Str('antsCT_', argstr='-o %s', usedefault=True,
+                             desc=('Prefix that is prepended to all output'
+                                   ' files (default = antsCT_)'))
+    image_suffix=traits.Str('nii.gz', desc=('any of standard ITK formats,'
+        ' nii.gz is default'),  argstr='-s %s', usedefault=True)
+    t1_registration_template = File(exists=True,
+            desc = ('Anatomical *intensity* template'
+           '(assumed to be skull-stripped). A common'
+           'case would be where this would be the same'
+           'template as specified in the -e option which'
+           'is not skull stripped.'),
+           argstr='-t %s',
+           mandatory=True)
+    extraction_registration_mask=File(exists=True, argstr='-f %s',
+                        desc=('Mask (defined in the template space) used during'
+                              ' registration for brain extraction.'))
+    keep_temporary_files=traits.Int(argstr='-k %d',
+            desc='Keep brain extraction/segmentation warps, etc (default = 0).')
+    max_iterations=traits.Int(argstr='-i %d',
+            desc=('ANTS registration max iterations'
+            '(default = 100x100x70x20)'))
+    prior_segmentation_weight=traits.Float(argstr='-w %f',
+            desc=('Atropos spatial prior *probability* weight for'
+            'the segmentation'))
+    segmentation_iterations=traits.Int(argstr='-n %d',
+            desc=('N4 -> Atropos -> N4 iterations during segmentation'
+            '(default = 3)'))
+    posterior_formulation=traits.Str(argstr='-b %s',
+            desc=('Atropos posterior formulation and whether or not'
+                'to use mixture model proportions.'
+                   '''e.g 'Socrates[1]' (default) or 'Aristotle[1]'.'''
+                   'Choose the latter if you'
+                   'want use the distance priors (see also the -l option'
+                   'for label propagation control).'))
+    use_floatingpoint_precision=traits.Enum(0, 1, argstr='-j %d',
+            desc='Use floating point precision in registrations (default = 0)')
+    use_random_seeding=traits.Enum(0, 1, argstr='-u %d',
+            desc=('Use random number generated from system clock in Atropos'
+            '(default = 1)'))
+    b_spline_smoothing=traits.Bool(argstr='-v',
+            desc=('Use B-spline SyN for registrations and B-spline'
+            'exponential mapping in DiReCT.'))
+    cortical_label_image=File(exists=True,
+            desc='Cortical ROI labels to use as a prior for ATITH.')
+    label_propagation=traits.Str(argstr='-l %s',
+            desc=('Incorporate a distance prior one the posterior formulation.  Should be'
+              '''of the form 'label[lambda,boundaryProbability]' where label'''
+              'is a value of 1,2,3,... denoting label ID.  The label'
+              'probability for anything outside the current label'
+              '  = boundaryProbability * exp( -lambda * distanceFromBoundary )'
+              'Intuitively, smaller lambda values will increase the spatial capture'
+              'range of the distance prior.  To apply to all label values, simply omit'
+              'specifying the label, i.e. -l [lambda,boundaryProbability].'))
+    quick_registration=traits.Bool(argstr='-q 1',
+            desc=('If = 1, use antsRegistrationSyNQuick.sh as the basis for registration'
+                  'during brain extraction, brain segmentation, and'
+                  '(optional) normalization to a template.'
+                  'Otherwise use antsRegistrationSyN.sh (default = 0).'))
+    debug=traits.Bool(argstr='-z 1',
+            desc=('If > 0, runs a faster version of the script.'
+            'Only for testing. Implies -u 0.'
+            'Requires single thread computation for complete reproducibility.'))
+
+class antsCorticalThicknessoutputSpec(TraitedSpec):
+    BrainExtractionMask=File(exists=True,
+            desc= 'brain extraction mask')
+    BrainSegmentation=File(exists=True,
+            desc='brain segmentaion image')
+    BrainSegmentationN4=File(exists=True,
+            desc='N4 corrected image')
+    BrainSegmentationPosteriorsCSF=File(exists=True,
+            desc='CSF posterior probability image')
+    BrainSegmentationPosteriorsGM=File(exists=True,
+            desc='GM posterior probability image')
+    BrainSegmentationPosteriorsWM=File(exists=True,
+            desc='WM posterior probability image')
+    BrainSegmentationPosteriorsDGM=File(exists=True,
+            desc='DGM posterior probability image')
+    CorticalThickness=File(exists=True,
+            desc='cortical thickness file')
+    TemplateToSubject1GenericAffine=File(exists=True,
+            desc='Template to subject affine')
+    TemplateToSubject0Warp=File(exists=True,
+            desc='Template to subject warp')
+    SubjectToTemplate1Warp=File(exists=True,
+            desc='Template to subject inverse warp')
+    SubjectToTemplate0GenericAffine=File(exists=True,
+            desc='Template to subject inverse affine')
+    TemplateToSubjectLogJacobian=File(exists=True,
+            desc='Template to subject log jacobian')
+
+class antsCorticalThickness(ANTSCommand):
+    """
+    Examples
+    --------
+    >>> from nipype.interfaces.ants.segmentation import antsCorticalThickness
+    >>> corticalthickness = antsCorticalThickness()
+    >>> corticalthickness.inputs.dimension = 3
+    >>> corticalthickness.inputs.anatomical_image ='T1.nii.gz'
+    >>> corticalthickness.inputs.brain_template = 'study_template.nii.gz'
+    >>> corticalthickness.inputs.brain_probability_mask ='ProbabilityMaskOfStudyTemplate.nii.gz'
+    >>> corticalthickness.inputs.segmentation_priors = ['BrainSegmentationPrior01.nii.gz', 'BrainSegmentationPrior02.nii.gz', 'BrainSegmentationPrior03.nii.gz', 'BrainSegmentationPrior04.nii.gz']
+    >>> corticalthickness.inputs.t1_registration_template = 'brain_study_template.nii.gz'
+    >>> corticalthickness.cmdline
+    'antsCorticalThickness.sh -a T1.nii.gz -m ProbabilityMaskOfStudyTemplate.nii.gz -e study_template.nii.gz -d 3 -s nii.gz -o antsCT_ -p BrainSegmentationPrior%02d.nii.gz -t brain_study_template.nii.gz'
+    """
+
+    input_spec = antsCorticalThicknessInputSpec
+    output_spec = antsCorticalThicknessoutputSpec
+    _cmd = 'antsCorticalThickness.sh'
+    def _format_arg(self, opt, spec, val):
+        if opt == 'anatomical_image':
+            retval = '-a %s' %(val)
+            return retval
+        if opt == 'brain_template':
+            retval = '-e %s' %(val)
+            return retval
+        if opt == 'brain_probability_mask':
+            retval = '-m %s' %(val)
+            return retval
+        if opt == 'out_prefix':
+            retval = '-o %s' %(val)
+            return retval
+        if opt == 't1_registration_template':
+            retval = '-t %s' %(val)
+            return retval
+        if opt == 'segmentation_priors':
+            _, _, ext = split_filename(self.inputs.segmentation_priors[0])
+            retval = "-p BrainSegmentationPrior%02d"
+            retval += ext
+            return retval
+        return super(ANTSCommand, self)._format_arg(opt, spec, val)
+
+    def _run_interface(self, runtime):
+        priors_directory = os.path.join(os.getcwd(), "priors")
+        if not os.path.exists(priors_directory):
+            os.makedirs(priors_directory)
+        _, _, ext = split_filename(self.inputs.segmentation_priors[0])
+        for i, f in enumerate(self.inputs.segmentation_priors):
+            target = os.path.join(priors_directory,
+                    'BrainSegmentationPrior%02d' % (i + 1) + ext)
+            if not (os.path.exists(target) and os.path.realpath(target) == os.path.abspath(f)):
+                copyfile(os.path.abspath(f), os.path.join(priors_directory,
+                    'BrainSegmentationPrior%02d' % (i + 1) + ext))
+        runtime = super(antsCorticalThickness, self)._run_interface(runtime)
+        return runtime
+
+    def _list_outputs(self):
+        outputs = self._outputs().get()
+        outputs['BrainExtractionMask'] = os.path.join(os.getcwd(),
+                                                        self.inputs.out_prefix +
+                                                        'BrainExtractionMask.'+
+                                                        self.inputs.image_suffix)
+        outputs['BrainSegmentation'] = os.path.join(os.getcwd(),
+                                             self.inputs.out_prefix +
+                                             'BrainSegmentation.' +
+                                             self.inputs.image_suffix)
+        outputs['BrainSegmentationN4'] = os.path.join(os.getcwd(),
+                                                     self.inputs.out_prefix +
+                                                     'BrainSegmentation0N4.' +
+                                                     self.inputs.image_suffix)
+        outputs['BrainSegmentationPosteriorsCSF'] = os.path.join(os.getcwd(),
+                                                 self.inputs.out_prefix +
+                                                 'BrainSegmentationPosteriors01.' +
+                                                 self.inputs.image_suffix)
+        outputs['BrainSegmentationPosteriorsGM'] = os.path.join(os.getcwd(),
+                                                  'BrainSegmentationPosteriors02.' +
+                                                  self.inputs.image_suffix)
+        outputs['BrainSegmentationPosteriorsWM'] = os.path.join(os.getcwd(),
+                                                  'BrainSegmentationPosteriors03.' +
+                                                   self.inputs.image_suffix)
+        outputs['BrainSegmentationPosteriorsDGM'] = os.path.join(os.getcwd(),
+                                                  'BrainSegmentationPosteriors04.' +
+                                                   self.inputs.image_suffix)
+        outputs['CorticalThickness'] = os.path.join(os.getcwd(),
+               self.inputs.out_prefix + 'CorticalThickness.' +
+               self.inputs.image_suffix)
+        outputs['TemplateToSubject1GenericAffine'] = os.path.join(
+                os.getcwd(),
+                'TemplateToSubject1GenericAffine.mat')
+        outputs['TemplateToSubject0Warp'] = os.path.join(os.getcwd(),
+               self.inputs.out_prefix + 'TemplateToSubject0Warp.'+
+               self.inputs.image_suffix)
+        outputs['SubjectToTemplate1Warp'] = os.path.join(os.getcwd(),
+               self.inputs.out_prefix + 'SubjectToTemplate1Warp' +
+               self.inputs.image_suffix)
+        outputs['SubjectToTemplate0GenericAffine'] = os.path.join(os.getcwd(),
+                self.inputs.out_prefix + 'SubjectToTemplate0GenericAffine.mat')
+        outputs['TemplateToSubjectLogJacobian'] = os.path.join(os.getcwd(),
+               self.inputs.out_prefix + 'subjectToTemplateLogJacobian.'+
+               self.inputs.image_suffix)
+        return outputs

nipype/interfaces/freesurfer/tests/test_auto_Tkregister2.py

@@ -10,27 +10,42 @@ def test_Tkregister2_inputs():
     ),
     fsl_in_matrix=dict(argstr='--fsl %s',
     ),
+    fsl_out=dict(argstr='--fslregout %s',
+    ),
+    fstal=dict(argstr='--fstal',
+    xor=['target_image', 'moving_image'],
+    ),
+    fstarg=dict(argstr='--fstarg',
+    xor=['target_image'],
+    ),
     ignore_exception=dict(nohash=True,
     usedefault=True,
     ),
     moving_image=dict(argstr='--mov %s',
     mandatory=True,
     ),
+    movscale=dict(argstr='--movscale %f',
+    ),
     noedit=dict(argstr='--noedit',
     usedefault=True,
     ),
     reg_file=dict(argstr='--reg %s',
     mandatory=True,
-    name_source='fsl',
-    name_template='%s.dat',
+    usedefault=True,
+    ),
+    reg_header=dict(argstr='--regheader',
     ),
     subject_id=dict(argstr='--s %s',
-    mandatory=True,
     ),
     subjects_dir=dict(),
+    target_image=dict(argstr='--targ %s',
+    xor=['fstarg'],
+    ),
     terminal_output=dict(mandatory=True,
     nohash=True,
     ),
+    xfm=dict(argstr='--xfm %s',
+    ),
     )
     inputs = Tkregister2.input_spec()
 
@@ -39,7 +54,8 @@ def test_Tkregister2_inputs():
             yield assert_equal, getattr(inputs.traits()[key], metakey), value
 
 def test_Tkregister2_outputs():
-    output_map = dict(reg_file=dict(),
+    output_map = dict(fsl_file=dict(),
+    reg_file=dict(),
     )
     outputs = Tkregister2.output_spec()
 

nipype/interfaces/freesurfer/utils.py

@@ -1191,39 +1191,89 @@ class ExtractMainComponent(CommandLine):
 
 
 class Tkregister2InputSpec(FSTraitedSpec):
+    target_image = File(exists=True, argstr="--targ %s",
+                        xor=['fstarg'],
+                        desc='target volume')
+    fstarg = traits.Bool(False, argstr='--fstarg',
+                         xor=['target_image'],
+                         desc='use subject\'s T1 as reference')
+
     moving_image = File(exists=True, mandatory=True, argstr="--mov %s",
-               desc='moving volume')
+                        desc='moving volume')
     fsl_in_matrix = File(exists=True, argstr="--fsl %s",
-               desc='fsl-style registration input matrix')
-    subject_id = traits.String(argstr="--s %s", mandatory=True,
+                         desc='fsl-style registration input matrix')
+    subject_id = traits.String(argstr="--s %s",
                                desc='freesurfer subject ID')
-    noedit = traits.Bool(True, argstr="--noedit", desc='do not open edit window (exit)', usedefault=True)
-    reg_file = File(name_template='%s.dat', name_source='fsl',
-                        mandatory=True, argstr="--reg %s",
-                        desc='freesurfer-style registration file')
+    noedit = traits.Bool(True, argstr="--noedit", usedefault=True,
+                         desc='do not open edit window (exit)')
+    reg_file = File('register.dat', usedefault=True,
+                    mandatory=True, argstr='--reg %s',
+                    desc='freesurfer-style registration file')
+    reg_header = traits.Bool(False, argstr='--regheader',
+                             desc='compute regstration from headers')
+    fstal = traits.Bool(False, argstr='--fstal',
+                        xor=['target_image', 'moving_image'],
+                        desc='set mov to be tal and reg to be tal xfm')
+    movscale = traits.Float(argstr='--movscale %f',
+                            desc='adjust registration matrix to scale mov')
+    xfm = File(exists=True, argstr='--xfm %s',
+               desc='use a matrix in MNI coordinates as initial registration')
+    fsl_out = File(argstr='--fslregout %s',
+                   desc='compute an FSL-compatible resgitration matrix')
 
 
 class Tkregister2OutputSpec(TraitedSpec):
     reg_file = File(exists=True, desc='freesurfer-style registration file')
+    fsl_file = File(desc='FSL-style registration file')
 
 
 class Tkregister2(FSCommand):
-    """Use tkregister2 without the manual editing stage to convert 
-    FSL-style registration matrix (.mat) to FreeSurfer-style registration matrix (.dat)
+    """
 
     Examples
     --------
 
+    Get transform matrix between orig (*tkRAS*) and native (*scannerRAS*)
+    coordinates in Freesurfer. Implements the first step of mapping surfaces
+    to native space in `this guide
+    <http://surfer.nmr.mgh.harvard.edu/fswiki/FsAnat-to-NativeAnat>`_.
+
+    >>> from nipype.interfaces.freesurfer import Tkregister2
+    >>> tk2 = Tkregister2(reg_file='T1_to_native.dat')
+    >>> tk2.inputs.moving_image = 'T1.mgz'
+    >>> tk2.inputs.target_image = 'structural.nii'
+    >>> tk2.inputs.reg_header = True
+    >>> tk2.cmdline
+    'tkregister2 --mov T1.mgz --noedit --reg T1_to_native.dat --regheader \
+--targ structural.nii'
+    >>> tk2.run() # doctest: +SKIP
+
+    The example below uses tkregister2 without the manual editing
+    stage to convert FSL-style registration matrix (.mat) to
+    FreeSurfer-style registration matrix (.dat)
+
     >>> from nipype.interfaces.freesurfer import Tkregister2
-    >>> tk2 = Tkregister2(reg_file='register.dat')
+    >>> tk2 = Tkregister2()
     >>> tk2.inputs.moving_image = 'epi.nii'
     >>> tk2.inputs.fsl_in_matrix = 'flirt.mat'
-    >>> tk2.inputs.subject_id = 'test_subject'
+    >>> tk2.cmdline
+    'tkregister2 --fsl flirt.mat --mov epi.nii --noedit --reg register.dat'
     >>> tk2.run() # doctest: +SKIP
     """
     _cmd = "tkregister2"
     input_spec = Tkregister2InputSpec
     output_spec = Tkregister2OutputSpec
-    
 
-    
+    def _list_outputs(self):
+        outputs = self._outputs().get()
+        outputs['reg_file'] = os.path.abspath(self.inputs.reg_file)
+        if isdefined(self.inputs.fsl_out):
+            outputs['fsl_file'] = op.abspath(self.inputs.fsl_out)
+        return outputs
+
+    def _gen_outfilename(self):
+        if isdefined(self.inputs.out_file):
+            return os.path.abspath(self.inputs.out_file)
+        else:
+            _, name, ext = split_filename(self.inputs.in_file)
+            return os.path.abspath(name + '_smoothed' + ext)