Add interpolation_parameters input, cleaned docs

Added interpolation_parameters input to allow for BSpline, Gaussian and
MultiLabel interpolation. Also cleaned up some doc typos. Fixed
winsorize format_arg to reset to false after second winsorize (docs
perform correctly).

Author: blakedewey

nipype/interfaces/ants/registration.py

@@ -285,11 +285,12 @@ class RegistrationInputSpec(ANTSCommandInputSpec):
         default=True, usedefault=True)
     interpolation = traits.Enum(
         'Linear', 'NearestNeighbor', 'CosineWindowedSinc', 'WelchWindowedSinc',
-        'HammingWindowedSinc', 'LanczosWindowedSinc', 'BSpline',
-        # MultiLabel[<sigma=imageSpacing>,<alpha=4.0>]
-        # Gaussian[<sigma=imageSpacing>,<alpha=1.0>]
-        # BSpline[<order=3>]
+        'HammingWindowedSinc', 'LanczosWindowedSinc', 'BSpline', 'MultiLabel', 'Gaussian',
         argstr='%s', usedefault=True)
+    interpolation_parameters = traits.Either(traits.Tuple(traits.Int()),  # BSpline (order)
+                                             traits.Tuple(traits.Float(),  # Gaussian/MultiLabel (sigma, alpha)
+                                                          traits.Float)
+                                             )
 
     write_composite_transform = traits.Bool(
         argstr='--write-composite-transform %d',
@@ -439,8 +440,8 @@ class Registration(ANTSCommand):
 
     >>> # Test collapse transforms flag
     >>> reg4 = copy.deepcopy(reg)
-    >>> reg.inputs.save_state = 'trans.mat'
-    >>> reg.inputs.restore_state = 'trans.mat'
+    >>> reg4.inputs.save_state = 'trans.mat'
+    >>> reg4.inputs.restore_state = 'trans.mat'
     >>> reg4.inputs.initialize_transforms_per_stage = True
     >>> reg4.inputs.collapse_output_transforms = True
     >>> outputs = reg4._list_outputs()
@@ -452,8 +453,10 @@ class Registration(ANTSCommand):
      'inverse_warped_image': <undefined>,
      'reverse_invert_flags': [],
      'reverse_transforms': [],
-     'save_state': <undefined>,
+     'save_state': '.../nipype/testing/data/trans.mat',
      'warped_image': '.../nipype/testing/data/output_warped_image.nii.gz'}
+    >>> reg4.cmdline
+    'antsRegistration --collapse-output-transforms 1 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 1 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --restore-state trans.mat --save-state trans.mat --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'
 
     >>> # Test collapse transforms flag
     >>> reg4b = copy.deepcopy(reg4)
@@ -468,9 +471,11 @@ class Registration(ANTSCommand):
      'inverse_warped_image': <undefined>,
      'reverse_invert_flags': [True, False],
      'reverse_transforms': ['.../nipype/testing/data/output_0GenericAffine.mat', '.../nipype/testing/data/output_1InverseWarp.nii.gz'],
-     'save_state': <undefined>,
+     'save_state': '.../nipype/testing/data/trans.mat',
      'warped_image': '.../nipype/testing/data/output_warped_image.nii.gz'}
     >>> reg4b.aggregate_outputs()  # doctest: +SKIP
+    >>> reg4b.cmdline
+    'antsRegistration --collapse-output-transforms 1 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 1 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --restore-state trans.mat --save-state trans.mat --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 0'
 
     >>> # Test multiple metrics per stage
     >>> reg5 = copy.deepcopy(reg)
@@ -482,14 +487,28 @@ class Registration(ANTSCommand):
     >>> reg5.inputs.sampling_strategy = ['Random', None] # use default strategy in second stage
     >>> reg5.inputs.sampling_percentage = [0.05, [0.05, 0.10]]
     >>> reg5.cmdline
-    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --restore-state trans.mat --save-state trans.mat --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 0.5, 32, None, 0.05 ] --metric CC[ fixed1.nii, moving1.nii, 0.5, 4, None, 0.1 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'
+    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 0.5, 32, None, 0.05 ] --metric CC[ fixed1.nii, moving1.nii, 0.5, 4, None, 0.1 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'
 
     >>> # Test multiple inputs
     >>> reg6 = copy.deepcopy(reg5)
     >>> reg6.inputs.fixed_image = ['fixed1.nii', 'fixed2.nii']
     >>> reg6.inputs.moving_image = ['moving1.nii', 'moving2.nii']
     >>> reg6.cmdline
-    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --restore-state trans.mat --save-state trans.mat --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 0.5, 32, None, 0.05 ] --metric CC[ fixed2.nii, moving2.nii, 0.5, 4, None, 0.1 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1   --write-composite-transform 1'
+    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Linear --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 0.5, 32, None, 0.05 ] --metric CC[ fixed2.nii, moving2.nii, 0.5, 4, None, 0.1 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'
+
+    >>> # Test Interpolation Parameters (BSpline)
+    >>> reg7a = copy.deepcopy(reg)
+    >>> reg7a.inputs.interpolation = 'BSpline'
+    >>> reg7a.inputs.interpolation_parameters = (3,)
+    >>> reg7a.cmdline
+    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation BSpline[ 3 ] --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'
+
+    >>> # Test Interpolation Parameters (MultiLabel/Gaussian)
+    >>> reg7b = copy.deepcopy(reg)
+    >>> reg7b.inputs.interpolation = 'Gaussian'
+    >>> reg7b.inputs.interpolation_parameters = (1.0, 1.0)
+    >>> reg7b.cmdline
+    'antsRegistration --collapse-output-transforms 0 --dimensionality 3 --initial-moving-transform [ trans.mat, 1 ] --initialize-transforms-per-stage 0 --interpolation Gaussian[ 1.0, 1.0 ] --output [ output_, output_warped_image.nii.gz ] --transform Affine[ 2.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32, Random, 0.05 ] --convergence [ 1500x200, 1e-08, 20 ] --smoothing-sigmas 1.0x0.0vox --shrink-factors 2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --transform SyN[ 0.25, 3.0, 0.0 ] --metric Mattes[ fixed1.nii, moving1.nii, 1, 32 ] --convergence [ 100x50x30, 1e-09, 20 ] --smoothing-sigmas 2.0x1.0x0.0vox --shrink-factors 3x2x1 --use-estimate-learning-rate-once 1 --use-histogram-matching 1 --winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'
     """
     DEF_SAMPLING_STRATEGY = 'None'
     """The default sampling strategy argument."""
@@ -669,7 +688,6 @@ def _formatWinsorizeImageIntensities(self):
         self._quantilesDone = True
         return '--winsorize-image-intensities [ %s, %s ]' % (self.inputs.winsorize_lower_quantile, self.inputs.winsorize_upper_quantile)
 
-
     def _format_arg(self, opt, spec, val):
         if opt == 'fixed_image_mask':
             if isdefined(self.inputs.moving_image_mask):
@@ -698,8 +716,13 @@ def _format_arg(self, opt, spec, val):
                                                                       0],
                                                                   doCenterOfMassInit)
         elif opt == 'interpolation':
-            # TODO: handle multilabel, gaussian, and bspline options
-            return '--interpolation %s' % self.inputs.interpolation
+            if self.inputs.interpolation in ['BSpline', 'MultiLabel', 'Gaussian'] and \
+                    isdefined(self.inputs.interpolation_parameters):
+                return '--interpolation %s[ %s ]' % (self.inputs.interpolation,
+                                                     ', '.join([str(param)
+                                                                for param in self.inputs.interpolation_parameters]))
+            else:
+                return '--interpolation %s' % self.inputs.interpolation
         elif opt == 'output_transform_prefix':
             out_filename = self._get_outputfilenames(inverse=False)
             inv_out_filename = self._get_outputfilenames(inverse=True)
@@ -715,7 +738,9 @@ def _format_arg(self, opt, spec, val):
         elif opt == 'winsorize_upper_quantile' or opt == 'winsorize_lower_quantile':
             if not self._quantilesDone:
                 return self._formatWinsorizeImageIntensities()
-            return ''  # Must return something for argstr!
+            else:
+                self._quantilesDone = False
+                return ''  # Must return something for argstr!
         # This feature was removed from recent versions of antsRegistration due to corrupt outputs.
         # elif opt == 'collapse_linear_transforms_to_fixed_image_header':
         #    return self._formatCollapseLinearTransformsToFixedImageHeader()

nipype/interfaces/ants/tests/test_auto_Registration.py

@@ -41,6 +41,7 @@ def test_Registration_inputs():
     interpolation=dict(argstr='%s',
     usedefault=True,
     ),
+    interpolation_parameters=dict(),
     invert_initial_moving_transform=dict(requires=['initial_moving_transform'],
     xor=['initial_moving_transform_com'],
     ),