ENH: Enable antsRegistration 2.2.0+ masking

docker/base.Dockerfile

@@ -77,6 +77,7 @@ RUN apt-get update && \
                     fsl-core \
                     fsl-mni152-templates \
                     afni \
+                    ants \
                     bzip2 \
                     xvfb \
                     git \
@@ -103,23 +104,16 @@ ENV FSLDIR=/usr/share/fsl/5.0 \
     AFNI_IMSAVE_WARNINGS=NO \
     AFNI_TTATLAS_DATASET=/usr/share/afni/atlases \
     AFNI_PLUGINPATH=/usr/lib/afni/plugins \
-    PATH=/usr/lib/fsl/5.0:/usr/lib/afni/bin:$PATH
-
-# Installing and setting up ANTs
-RUN mkdir -p /opt/ants && \
-    curl -sSL "https://dl.dropbox.com/s/2f4sui1z6lcgyek/ANTs-Linux-centos5_x86_64-v2.2.0-0740f91.tar.gz?dl=0" \
-    | tar -zx -C /opt
-
-ENV ANTSPATH=/opt/ants \
-    PATH=$ANTSPATH:$PATH
+    ANTSPATH=/usr/lib/ants
+ENV PATH=/usr/lib/fsl/5.0:/usr/lib/afni/bin:$ANTSPATH:$PATH
 
 # Installing and setting up c3d
 RUN mkdir -p /opt/c3d && \
     curl -sSL "http://downloads.sourceforge.net/project/c3d/c3d/1.0.0/c3d-1.0.0-Linux-x86_64.tar.gz" \
     | tar -xzC /opt/c3d --strip-components 1
 
-ENV C3DPATH=/opt/c3d/ \
-    PATH=$C3DPATH/bin:$PATH
+ENV C3DPATH=/opt/c3d/
+ENV PATH=$C3DPATH/bin:$PATH
 
 # Install fake-S3
 ENV GEM_HOME /usr/lib/ruby/gems/2.3

nipype/interfaces/ants/base.py

@@ -4,8 +4,12 @@
 """The ants module provides basic functions for interfacing with ANTS tools."""
 from __future__ import print_function, division, unicode_literals, absolute_import
 from builtins import str
+
+import os
+import subprocess
+
 # Local imports
-from ... import logging
+from ... import logging, LooseVersion
 from ..base import CommandLine, CommandLineInputSpec, traits, isdefined
 logger = logging.getLogger('interface')
 
@@ -25,6 +29,39 @@
 ALT_ITKv4_THREAD_LIMIT_VARIABLE = 'ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS'
 
 
+class Info(object):
+    _version = None
+
+    @property
+    def version(self):
+        if self._version is None:
+            try:
+                basedir = os.environ['ANTSPATH']
+            except KeyError:
+                return None
+
+            cmd = os.path.join(basedir, 'antsRegistration')
+            try:
+                res = subprocess.check_output([cmd, '--version']).decode('utf-8')
+            except OSError:
+                return None
+
+            for line in res.splitlines():
+                if line.startswith('ANTs Version: '):
+                    self._version = line.split()[2]
+                    break
+            else:
+                return None
+
+        v_string, githash = self._version.split('-')
+
+        # 2.2.0-equivalent version string
+        if 'post' in v_string and LooseVersion(v_string) >= LooseVersion('2.1.0.post789'):
+            return '2.2.0'
+        else:
+            return '.'.join(v_string.split('.')[:3])
+
+
 class ANTSCommandInputSpec(CommandLineInputSpec):
     """Base Input Specification for all ANTS Commands
     """
@@ -84,3 +121,7 @@ def set_default_num_threads(cls, num_threads):
         <instance>.inputs.num_threads
         """
         cls._num_threads = num_threads
+
+    @property
+    def version(self):
+        return Info().version

nipype/interfaces/ants/registration.py

@@ -11,6 +11,7 @@
 from builtins import range, str
 import os
 
+from ...utils.filemanip import filename_to_list
 from ..base import TraitedSpec, File, Str, traits, InputMultiPath, isdefined
 from .base import ANTSCommand, ANTSCommandInputSpec
 
@@ -219,12 +220,22 @@ class RegistrationInputSpec(ANTSCommandInputSpec):
                             usedefault=True, desc='image dimension (2 or 3)')
     fixed_image = InputMultiPath(File(exists=True), mandatory=True,
                                  desc='image to apply transformation to (generally a coregistered functional)')
-    fixed_image_mask = File(argstr='%s', exists=True,
-                            desc='mask used to limit metric sampling region of the fixed image')
+    fixed_image_mask = File(
+        exists=True, argstr='%s', max_ver='2.1.0', xor=['fixed_image_masks'],
+        desc='mask used to limit metric sampling region of the fixed image')
+    fixed_image_masks = InputMultiPath(
+        traits.Either('NULL', File(exists=True)), min_ver='2.2.0', xor=['fixed_image_mask'],
+        desc='mask used to limit metric sampling region of the fixed image '
+        '(Use "NULL" to omit a mask at a given stage)')
     moving_image = InputMultiPath(File(exists=True), mandatory=True,
                                   desc='image to apply transformation to (generally a coregistered functional)')
-    moving_image_mask = File(requires=['fixed_image_mask'],
-                             exists=True, desc='mask used to limit metric sampling region of the moving image')
+    moving_image_mask = File(
+        exists=True, requires=['fixed_image_mask'], max_ver='2.1.0', xor=['moving_image_masks'],
+        desc='mask used to limit metric sampling region of the moving image')
+    moving_image_masks = InputMultiPath(
+        traits.Either('NULL', File(exists=True)), min_ver='2.2.0', xor=['moving_image_mask'],
+        desc='mask used to limit metric sampling region of the moving image '
+        '(Use "NULL" to omit a mask at a given stage)')
 
     save_state = File(argstr='--save-state %s', exists=False,
                       desc='Filename for saving the internal restorable state of the registration')
@@ -648,6 +659,20 @@ class Registration(ANTSCommand):
 --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 masking
+    >>> reg9 = copy.deepcopy(reg)
+    >>> reg9.inputs.fixed_image_masks = ['NULL', 'fixed1.nii']
+    >>> reg9.cmdline # doctest: +ALLOW_UNICODE
+    '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 --masks [ NULL, NULL ] \
+--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 --masks [ fixed1.nii, NULL ] \
+--winsorize-image-intensities [ 0.0, 1.0 ]  --write-composite-transform 1'
     """
     DEF_SAMPLING_STRATEGY = 'None'
     """The default sampling strategy argument."""
@@ -783,6 +808,20 @@ def _format_registration(self):
             if isdefined(self.inputs.restrict_deformation):
                 retval.append('--restrict-deformation %s' %
                               self._format_xarray(self.inputs.restrict_deformation[ii]))
+            if any((isdefined(self.inputs.fixed_image_masks),
+                    isdefined(self.inputs.moving_image_masks))):
+                if isdefined(self.inputs.fixed_image_masks):
+                    fixed_masks = filename_to_list(self.inputs.fixed_image_masks)
+                    fixed_mask = fixed_masks[ii if len(fixed_masks) > 1 else 0]
+                else:
+                    fixed_mask = 'NULL'
+
+                if isdefined(self.inputs.moving_image_masks):
+                    moving_masks = filename_to_list(self.inputs.moving_image_masks)
+                    moving_mask = moving_masks[ii if len(moving_masks) > 1 else 0]
+                else:
+                    moving_mask = 'NULL'
+                retval.append('--masks [ %s, %s ]' % (fixed_mask, moving_mask))
         return " ".join(retval)
 
     def _get_outputfilenames(self, inverse=False):

nipype/interfaces/ants/tests/test_auto_Registration.py

@@ -24,6 +24,11 @@ def test_Registration_inputs():
     fixed_image=dict(mandatory=True,
     ),
     fixed_image_mask=dict(argstr='%s',
+    max_ver='2.1.0',
+    xor=['fixed_image_masks'],
+    ),
+    fixed_image_masks=dict(min_ver='2.2.0',
+    xor=['fixed_image_mask'],
     ),
     float=dict(argstr='--float %d',
     ),
@@ -58,7 +63,12 @@ def test_Registration_inputs():
     metric_weight_stage_trait=dict(),
     moving_image=dict(mandatory=True,
     ),
-    moving_image_mask=dict(requires=['fixed_image_mask'],
+    moving_image_mask=dict(max_ver='2.1.0',
+    requires=['fixed_image_mask'],
+    xor=['moving_image_masks'],
+    ),
+    moving_image_masks=dict(min_ver='2.2.0',
+    xor=['moving_image_mask'],
     ),
     num_threads=dict(nohash=True,
     usedefault=True,