MAINT: Finalize upstreaming of ants interfaces to nipype

After nipy/nipype#3210 the ``niworkflows/interfaces/ants.py`` can go in
whole.

Author: oesteban

niworkflows/anat/ants.py

@@ -12,22 +12,23 @@
 # nipype
 from nipype.pipeline import engine as pe
 from nipype.interfaces import utility as niu
-from nipype.interfaces.fsl.maths import ApplyMask
-from nipype.interfaces.ants import N4BiasFieldCorrection, Atropos, MultiplyImages
-
-from ..utils.misc import get_template_specs
-# niworkflows
-from ..interfaces.ants import (
-    ImageMath,
-    ResampleImageBySpacing,
+from nipype.interfaces.ants import (
     AI,
+    Atropos,
+    MultiplyImages,
+    N4BiasFieldCorrection,
+    ResampleImageBySpacing,
     ThresholdImage,
 )
+
+from ..utils.misc import get_template_specs
+# niworkflows
+from ..interfaces.nibabel import ApplyMask
+from ..interfaces.ants import ImageMath
 from ..interfaces.fixes import (
     FixHeaderRegistration as Registration,
     FixHeaderApplyTransforms as ApplyTransforms,
 )
-from ..interfaces.utils import CopyXForm
 from ..interfaces.nibabel import Binarize
 
 
@@ -53,19 +54,21 @@
 }
 
 
-def init_brain_extraction_wf(name='brain_extraction_wf',
-                             in_template='OASIS30ANTs',
-                             template_spec=None,
-                             use_float=True,
-                             normalization_quality='precise',
-                             omp_nthreads=None,
-                             mem_gb=3.0,
-                             bids_suffix='T1w',
-                             atropos_refine=True,
-                             atropos_use_random_seed=True,
-                             atropos_model=None,
-                             use_laplacian=True,
-                             bspline_fitting_distance=200):
+def init_brain_extraction_wf(
+    name='brain_extraction_wf',
+    in_template='OASIS30ANTs',
+    template_spec=None,
+    use_float=True,
+    normalization_quality='precise',
+    omp_nthreads=None,
+    mem_gb=3.0,
+    bids_suffix='T1w',
+    atropos_refine=True,
+    atropos_use_random_seed=True,
+    atropos_model=None,
+    use_laplacian=True,
+    bspline_fitting_distance=200
+):
     """
     Build a workflow for atlas-based brain extraction on anatomical MRI data.
 
@@ -201,10 +204,6 @@ def init_brain_extraction_wf(name='brain_extraction_wf',
                 'out_segm', 'out_tpms']),
         name='outputnode')
 
-    copy_xform = pe.Node(CopyXForm(
-        fields=['out_file', 'out_mask', 'bias_corrected', 'bias_image']),
-        name='copy_xform', run_without_submitting=True)
-
     trunc = pe.MapNode(ImageMath(operation='TruncateImageIntensity', op2='0.01 0.999 256'),
                        name='truncate_images', iterfield=['op1'])
     inu_n4 = pe.MapNode(
@@ -296,7 +295,6 @@ def init_brain_extraction_wf(name='brain_extraction_wf',
 
     wf.connect([
         (inputnode, trunc, [('in_files', 'op1')]),
-        (inputnode, copy_xform, [(('in_files', _pop), 'hdr_file')]),
         (inputnode, inu_n4_final, [('in_files', 'input_image')]),
         (inputnode, init_aff, [('in_mask', 'fixed_image_mask')]),
         (inputnode, norm, [('in_mask', fixed_mask_trait)]),
@@ -314,16 +312,11 @@ def init_brain_extraction_wf(name='brain_extraction_wf',
         (thr_brainmask, dil_brainmask, [('output_image', 'op1')]),
         (dil_brainmask, get_brainmask, [('output_image', 'op1')]),
         (inu_n4_final, apply_mask, [('output_image', 'in_file')]),
-        (get_brainmask, apply_mask, [('output_image', 'mask_file')]),
-        (get_brainmask, copy_xform, [('output_image', 'out_mask')]),
-        (apply_mask, copy_xform, [('out_file', 'out_file')]),
-        (inu_n4_final, copy_xform, [('output_image', 'bias_corrected'),
+        (get_brainmask, apply_mask, [('output_image', 'in_mask')]),
+        (get_brainmask, outputnode, [('output_image', 'out_mask')]),
+        (inu_n4_final, outputnode, [('output_image', 'bias_corrected'),
                                     ('bias_image', 'bias_image')]),
-        (copy_xform, outputnode, [
-            ('out_file', 'out_file'),
-            ('out_mask', 'out_mask'),
-            ('bias_corrected', 'bias_corrected'),
-            ('bias_image', 'bias_image')]),
+        (apply_mask, outputnode, [('out_file', 'out_file')]),
     ])
 
     if use_laplacian:
@@ -363,8 +356,8 @@ def init_brain_extraction_wf(name='brain_extraction_wf',
                          run_without_submitting=True)
 
         wf.disconnect([
-            (get_brainmask, apply_mask, [('output_image', 'mask_file')]),
-            (copy_xform, outputnode, [('out_mask', 'out_mask')]),
+            (get_brainmask, apply_mask, [('output_image', 'in_mask')]),
+            (get_brainmask, outputnode, [('output_image', 'out_mask')]),
         ])
         wf.connect([
             (inu_n4, atropos_wf, [
@@ -376,7 +369,7 @@ def init_brain_extraction_wf(name='brain_extraction_wf',
             (atropos_wf, sel_wm, [('outputnode.out_tpms', 'inlist')]),
             (sel_wm, inu_n4_final, [('out', 'weight_image')]),
             (atropos_wf, apply_mask, [
-                ('outputnode.out_mask', 'mask_file')]),
+                ('outputnode.out_mask', 'in_mask')]),
             (atropos_wf, outputnode, [
                 ('outputnode.out_mask', 'out_mask'),
                 ('outputnode.out_segm', 'out_segm'),
@@ -451,7 +444,6 @@ def init_atropos_wf(name='atropos_wf',
     out_tpms : str
         Output :abbr:`TPMs (tissue probability maps)`
 
-
     """
     wf = pe.Workflow(name)
 
@@ -460,10 +452,6 @@ def init_atropos_wf(name='atropos_wf',
     outputnode = pe.Node(niu.IdentityInterface(
         fields=['out_mask', 'out_segm', 'out_tpms']), name='outputnode')
 
-    copy_xform = pe.Node(CopyXForm(
-        fields=['out_mask', 'out_segm', 'out_tpms']),
-        name='copy_xform', run_without_submitting=True)
-
     # Run atropos (core node)
     atropos = pe.Node(Atropos(
         dimension=3,
@@ -561,14 +549,13 @@ def init_atropos_wf(name='atropos_wf',
     depad_csf = pe.Node(ImageMath(operation='PadImage', op2='-%d' % padding),
                         name='27_depad_csf')
 
-    msk_conform = pe.Node(niu.Function(function=_conform_mask), name='msk_conform')
+    msk_dtype = pe.Node(niu.Function(function=_ensure_dtype), name='msk_dtype')
+    msk_dtype.inputs.dtype = 'uint8'
     merge_tpms = pe.Node(niu.Merge(in_segmentation_model[0]), name='merge_tpms')
     wf.connect([
-        (inputnode, copy_xform, [(('in_files', _pop), 'hdr_file')]),
         (inputnode, pad_mask, [('in_mask', 'op1')]),
         (inputnode, atropos, [('in_files', 'intensity_images'),
                               ('in_mask_dilated', 'mask_image')]),
-        (inputnode, msk_conform, [(('in_files', _pop), 'in_reference')]),
         (atropos, pad_segm, [('classified_image', 'op1')]),
         (pad_segm, sel_labels, [('output_image', 'in_segm')]),
         (sel_labels, get_wm, [('out_wm', 'op1')]),
@@ -602,14 +589,10 @@ def init_atropos_wf(name='atropos_wf',
         (depad_csf, merge_tpms, [('output_image', 'in1')]),
         (depad_gm, merge_tpms, [('output_image', 'in2')]),
         (depad_wm, merge_tpms, [('output_image', 'in3')]),
-        (depad_mask, msk_conform, [('output_image', 'in_mask')]),
-        (msk_conform, copy_xform, [('out', 'out_mask')]),
-        (depad_segm, copy_xform, [('output_image', 'out_segm')]),
-        (merge_tpms, copy_xform, [('out', 'out_tpms')]),
-        (copy_xform, outputnode, [
-            ('out_mask', 'out_mask'),
-            ('out_segm', 'out_segm'),
-            ('out_tpms', 'out_tpms')]),
+        (depad_mask, msk_dtype, [('output_image', 'in_mask')]),
+        (msk_dtype, outputnode, [('out', 'out_mask')]),
+        (depad_segm, outputnode, [('output_image', 'out_segm')]),
+        (merge_tpms, outputnode, [('out', 'out_tpms')]),
     ])
     return wf
 
@@ -799,34 +782,25 @@ def _select_labels(in_segm, labels):
     return out_files
 
 
-def _conform_mask(in_mask, in_reference):
-    """Ensures the mask headers make sense and match those of the T1w"""
+def _ensure_dtype(in_mask, dtype='uint8'):
+    """Ensure the mask headers make sense and match those of the T1w."""
     from pathlib import Path
     import numpy as np
     import nibabel as nb
     from nipype.utils.filemanip import fname_presuffix
 
-    ref = nb.load(in_reference)
     nii = nb.load(in_mask)
     hdr = nii.header.copy()
-    hdr.set_data_dtype('int16')
+    hdr.set_data_dtype(dtype)
     hdr.set_slope_inter(1, 0)
 
-    qform, qcode = ref.header.get_qform(coded=True)
-    if qcode is not None:
-        hdr.set_qform(qform, int(qcode))
-
-    sform, scode = ref.header.get_sform(coded=True)
-    if scode is not None:
-        hdr.set_sform(sform, int(scode))
-
     if '_maths' in in_mask:  # Cut the name at first _maths occurrence
         ext = ''.join(Path(in_mask).suffixes)
         basename = Path(in_mask).name
         in_mask = basename.split('_maths')[0] + ext
 
     out_file = fname_presuffix(in_mask, suffix='_mask',
                                newpath=str(Path()))
-    nii.__class__(np.asanyarray(nii.dataobj).astype('int16'), ref.affine,
+    nii.__class__(np.asanyarray(nii.dataobj).astype(dtype), nii.affine,
                   hdr).to_filename(out_file)
     return out_file

niworkflows/func/util.py

@@ -6,11 +6,11 @@
 
 from nipype.pipeline import engine as pe
 from nipype.interfaces import utility as niu, fsl, afni
+from nipype.interfaces.ants.utils import AI
 
 from templateflow.api import get as get_template
 
 from ..engine.workflows import LiterateWorkflow as Workflow
-from ..interfaces.ants import AI
 from ..interfaces.fixes import (
     FixHeaderRegistration as Registration,
     FixHeaderApplyTransforms as ApplyTransforms,