ENH: ROBEX interface

.mailmap

@@ -121,6 +121,7 @@ Kshitij Chawla <[email protected]>
 Leonie Lampe <[email protected]>
 Lukas Snoek <[email protected]>
 Marcel Falkiewicz <[email protected]> <[email protected]>
+Maria de Fatima Dias <[email protected]> <[email protected]>
 Martin Perez-Guevara <[email protected]>
 Mathias Goncalves <[email protected]> <[email protected]>
 Mathias Goncalves <[email protected]> <[email protected]>

.zenodo.json

@@ -263,7 +263,7 @@
     },
     {
       "affiliation": "CIBIT, UC",
-      "name": "Machado, F\u00e1tima",
+      "name": "Dias, Maria de Fatima",
       "orcid": "0000-0001-8878-1750"
     },
     {

nipype/interfaces/robex/preprocess.py

@@ -0,0 +1,69 @@
+import os
+from pathlib import Path
+
+from nipype.interfaces.base import (
+    TraitedSpec,
+    CommandLineInputSpec,
+    CommandLine,
+    File,
+    traits,
+    isdefined,
+)
+from nipype.utils.filemanip import split_filename
+
+
+class RobexInputSpec(CommandLineInputSpec):
+    in_file = File(
+        desc="Input volume", exists=True, mandatory=True, position=0, argstr="%s"
+    )
+    out_file = File(
+        desc="Output volume",
+        position=1,
+        argstr="%s",
+        hash_files=False,
+        name_template='%s_brain',
+        name_source=["in_file"],
+        keep_extension=True,
+    )
+    out_mask = File(
+        desc="Output mask",
+        position=2,
+        argstr="%s",
+        hash_files=False,
+        name_template='%s_brainmask',
+        name_source=["in_file"],
+        keep_extension=True,
+    )
+    seed = traits.Int(desc="Seed for random number generator", position=3, argstr="%i")
+
+
+class RobexOutputSpec(TraitedSpec):
+    out_file = File(desc="Output volume")
+    out_mask = File(desc="Output mask")
+
+
+class RobexSegment(CommandLine):
+    """
+
+    ROBEX is an automatic whole-brain extraction tool for T1-weighted MRI data (commonly known as skull stripping).
+    ROBEX aims for robust skull-stripping across datasets with no parameter settings. It fits a triangular mesh,
+    constrained by a shape model, to the probabilistic output of a supervised brain boundary classifier.
+    Because the shape model cannot perfectly accommodate unseen cases, a small free deformation is subsequently allowed.
+    The deformation is optimized using graph cuts.
+    The method ROBEX is based on was published in IEEE Transactions on Medical Imaging;
+    please visit the website http://www.jeiglesias.com to download the paper.
+
+    Examples
+    --------
+    >>> from nipype.interfaces.robex.preprocess import RobexSegment
+    >>> robex = RobexSegment()
+    >>> robex.inputs.in_file = 'structural.nii'
+    >>> robex.cmdline
+    'runROBEX.sh structural.nii structural_brain.nii structural_brainmask.nii'
+    >>> robex.run() # doctest: +SKIP
+
+    """
+
+    input_spec = RobexInputSpec
+    output_spec = RobexOutputSpec
+    _cmd = 'runROBEX.sh'

nipype/interfaces/robex/tests/test_auto_RobexSegment.py

@@ -0,0 +1,63 @@
+# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
+from ..preprocess import RobexSegment
+
+
+def test_RobexSegment_inputs():
+    input_map = dict(
+        args=dict(
+            argstr="%s",
+        ),
+        environ=dict(
+            nohash=True,
+            usedefault=True,
+        ),
+        in_file=dict(
+            argstr="%s",
+            extensions=None,
+            mandatory=True,
+            position=0,
+        ),
+        out_file=dict(
+            argstr="%s",
+            extensions=None,
+            hash_files=False,
+            keep_extension=True,
+            name_source=["in_file"],
+            name_template="%s_brain",
+            position=1,
+        ),
+        out_mask=dict(
+            argstr="%s",
+            extensions=None,
+            hash_files=False,
+            keep_extension=True,
+            name_source=["in_file"],
+            name_template="%s_brainmask",
+            position=2,
+        ),
+        seed=dict(
+            argstr="%i",
+            position=3,
+        ),
+    )
+    inputs = RobexSegment.input_spec()
+
+    for key, metadata in list(input_map.items()):
+        for metakey, value in list(metadata.items()):
+            assert getattr(inputs.traits()[key], metakey) == value
+
+
+def test_RobexSegment_outputs():
+    output_map = dict(
+        out_file=dict(
+            extensions=None,
+        ),
+        out_mask=dict(
+            extensions=None,
+        ),
+    )
+    outputs = RobexSegment.output_spec()
+
+    for key, metadata in list(output_map.items()):
+        for metakey, value in list(metadata.items()):
+            assert getattr(outputs.traits()[key], metakey) == value