add ANTs' KellyKapowski interface

Author: alexsavio

nipype/interfaces/ants/segmentation.py

@@ -12,6 +12,7 @@
 from builtins import range, str
 
 import os
+from ...external.due import BibTeX
 from ...utils.filemanip import split_filename, copyfile
 from ..base import TraitedSpec, File, traits, InputMultiPath, OutputMultiPath, isdefined
 from .base import ANTSCommand, ANTSCommandInputSpec
@@ -1153,3 +1154,167 @@ def _list_outputs(self):
                 self.inputs.out_atlas_voting_weight_name_format)
 
         return outputs
+
+
+class KellyKapowskiInputSpec(ANTSCommandInputSpec):
+    dimension = traits.Enum(3, 2, argstr='--image-dimensionality %d',
+                            usedefault=True,
+                            desc='image dimension (2 or 3)')
+
+    segmentation_image = File(exists=True, argstr='--segmentation-image "%s"',
+                              mandatory=True,
+                              desc="A segmentation image must be supplied labeling the gray and white matters.\n"
+                                   "Default values = 2 and 3, respectively.",)
+
+    gray_matter_label = traits.Int(2, usedefault=True,
+                                   desc="The label value for the gray matter label in the segmentation_image.")
+
+    white_matter_label = traits.Int(3, usedefault=True,
+                                    desc="The label value for the white matter label in the segmentation_image.")
+
+    gray_matter_prob_image = File(exists=True, argstr='--gray-matter-probability-image "%s"',
+                                  desc="In addition to the segmentation image, a gray matter probability image can be\n"
+                                       "used. If no such image is supplied, one is created using the segmentation image\n"
+                                       "and a variance of 1.0 mm.")
+
+    white_matter_prob_image = File(exists=True, argstr='--white-matter-probability-image "%s"',
+                                   desc="In addition to the segmentation image, a white matter probability image can be\n"
+                                       "used. If no such image is supplied, one is created using the segmentation image\n"
+                                       "and a variance of 1.0 mm.")
+
+    convergence = traits.Str(default="[50,0.001,10]", argstr='--convergence "%s"', usedefault=True,
+                             desc="Convergence is determined by fitting a line to the normalized energy profile of\n"
+                                  "the last N iterations (where N is specified by the window size) and determining\n"
+                                  "the slope which is then compared with the convergence threshold.",)
+
+    thickness_prior_estimate = traits.Float(10, usedefault=True, argstr="--thickness-prior-estimate %f",
+                                            desc="Provides a prior constraint on the final thickness measurement in mm.")
+
+    thickness_prior_image = File(exists=True, argstr='--thickness-prior-image "%s"',
+                                 desc="An image containing spatially varying prior thickness values.")
+
+    gradient_step = traits.Float(0.025, usedefault=True, argstr="--gradient-step %f",
+                                 desc="Gradient step size for the optimization.")
+
+    smoothing_variance = traits.Float(1.0, argstr="--smoothing-variance %f",
+                                      desc="Defines the Gaussian smoothing of the hit and total images.")
+
+    smoothing_velocity_field = traits.Float(1.5, argstr="--smoothing-velocity-field-parameter %f",
+                                            desc="Defines the Gaussian smoothing of the velocity field (default = 1.5).\n"
+                                            "If the b-spline smoothing option is chosen, then this defines the \n"
+                                            "isotropic mesh spacing for the smoothing spline (default = 15).")
+
+    use_bspline_smoothing = traits.Bool(argstr="--use-bspline-smoothing 1",
+                                        desc="Sets the option for B-spline smoothing of the velocity field.")
+
+    number_integration_points = traits.Int(10, argstr="--number-of-integration-points %d",
+                                           desc="Number of compositions of the diffeomorphism per iteration.")
+
+    max_invert_displacement_field_iters = traits.Int(20, argstr="--maximum-number-of-invert-displacement-field-iterations %d",
+                                                     desc="Maximum number of iterations for estimating the invert \n"
+                                                          "displacement field.")
+
+    cortical_thickness = File(argstr='--output "%s"', genfile=True,
+                              desc='Filename for the cortical thickness.', hash_files=False)
+
+    warped_white_matter = File(desc='Filename for the warped white matter file.', hash_files=False)
+
+
+class KellyKapowskiOutputSpec(TraitedSpec):
+    cortical_thickness = File(desc="A thickness map defined in the segmented gray matter.")
+    warped_white_matter = File(desc="A warped white matter image.")
+
+
+class KellyKapowski(ANTSCommand):
+    """ Nipype Interface to ANTs' KellyKapowski, also known as DiReCT.
+
+    DiReCT is a registration based estimate of cortical thickness. It was published
+    in S. R. Das, B. B. Avants, M. Grossman, and J. C. Gee, Registration based
+    cortical thickness measurement, Neuroimage 2009, 45:867--879.
+
+    Examples
+    --------
+    >>> from pypes.interfaces.ants import KellyKapowski
+    >>> kk = KellyKapowski()
+    >>> kk.inputs.dimension = 3
+    >>> kk.inputs.segmentation_image = "anat_hc_gm_wm.nii.gz"
+    >>> kk.inputs.gray_matter_prob_image = "anat_hc_gm.nii.gz"
+    >>> kk.inputs.white_matter_prob_image = "anat_hc_wm.nii.gz"
+    >>> kk.inputs.convergence = "[45,0.0,10]"
+    >>> kk.inputs.gradient_step = 0.025
+    >>> kk.inputs.smoothing_variance = 1.0
+    >>> kk.inputs.smoothing_velocity_field = 1.5
+    >>> #kk.inputs.use_bspline_smoothing = False
+    >>> kk.inputs.number_integration_points = 10
+    >>> kk.inputs.thickness_prior_estimate = 10
+    >>> kk.cmdline # doctest: +ALLOW_UNICODE
+    'KellyKapowski --convergence "[45,0.0,10]"
+    --output "[anat_hc_gm_wm_cortical_thickness.nii.gz, anat_hc_gm_wm_warped_white_matter.nii.gz]"
+    --image-dimensionality 3 --gradient-step 0.025000 --gray-matter-probability-image "anat_hc_gm.nii.gz"
+    --number-of-integration-points 10 --segmentation-image "[anat_hc_gm_wm.nii.gz,2,3]" --smoothing-variance 1.000000
+    --smoothing-velocity-field-parameter 1.500000 --thickness-prior-estimate 10.000000
+    --white-matter-probability-image "anat_hc_wm.nii.gz"'
+    """
+    _cmd = "KellyKapowski"
+    input_spec = KellyKapowskiInputSpec
+    output_spec = KellyKapowskiOutputSpec
+
+    references_ = [{'entry': BibTeX("@book{Das2009867,"
+                                    "author={Sandhitsu R. Das and Brian B. Avants and Murray Grossman and James C. Gee},"
+                                    "title={Registration based cortical thickness measurement.},"
+                                    "journal={NeuroImage},"
+                                    "volume={45},"
+                                    "number={37},"
+                                    "pages={867--879},"
+                                    "year={2009},"
+                                    "issn={1053-8119},"
+                                    "url={http://www.sciencedirect.com/science/article/pii/S1053811908012780},"
+                                    "doi={http://dx.doi.org/10.1016/j.neuroimage.2008.12.016}"
+                                    "}"),
+                    'description': 'The details on the implementation of DiReCT.',
+                    'tags': ['implementation'],
+                    }]
+
+    def _parse_inputs(self, skip=None):
+        if skip is None:
+            skip = []
+        skip += ['warped_white_matter', 'gray_matter_label', 'white_matter_label']
+        return super(KellyKapowski, self)._parse_inputs(skip=skip)
+
+    def _list_outputs(self):
+        outputs = self._outputs().get()
+        outputs['cortical_thickness']  = os.path.abspath(self._gen_filename('cortical_thickness'))
+        outputs['warped_white_matter'] = os.path.abspath(self._gen_filename('warped_white_matter'))
+        return outputs
+
+    def _gen_filename(self, name):
+        if name == 'cortical_thickness':
+            output = self.inputs.cortical_thickness
+            if not isdefined(output):
+                _, name, ext = split_filename(self.inputs.segmentation_image)
+                output = name + '_cortical_thickness' + ext
+            return output
+
+        if name == 'warped_white_matter':
+            output = self.inputs.warped_white_matter
+            if not isdefined(output):
+                _, name, ext = split_filename(self.inputs.segmentation_image)
+                output = name + '_warped_white_matter' + ext
+            return output
+
+        return None
+
+    def _format_arg(self, opt, spec, val):
+        if opt == "segmentation_image":
+            newval = '[{0},{1},{2}]'.format(self.inputs.segmentation_image,
+                                            self.inputs.gray_matter_label,
+                                            self.inputs.white_matter_label)
+            return spec.argstr % newval
+
+        if opt == "cortical_thickness":
+            ct = self._gen_filename("cortical_thickness")
+            wm = self._gen_filename("warped_white_matter")
+            newval = '[{}, {}]'.format(ct, wm)
+            return spec.argstr % newval
+
+        return super(KellyKapowski, self)._format_arg(opt, spec, val)