ENH: Add new command line for BRAINSABC tool

A new featue set has been added to improve
the quality of segmentations from the BRAINSABC
tool.

purePlugsThreshold = If this threshold value is greater than zero, only pure samples are used to compute the distributions in EM classification, and only pure samples are used for KNN training. The default value is set to 0, that means not using pure plugs. However, a value of 0.2 is suggested if you want to activate using pure plugs option.

numberOfSubSamplesInEachPlugArea -- How many samples in each plug area to create.

Author: hjmjohnson

CHANGES

@@ -1,6 +1,7 @@
 Next release
 ============
 
+* ENH: Add new flags to the BRAINSABC for new featues (https://github.com/nipy/nipype/pull/1322)
 * ENH: Provides a Nipype wrapper for ANTs DenoiseImage (https://github.com/nipy/nipype/pull/1291)
 * FIX: Minor bugfix logging hash differences (https://github.com/nipy/nipype/pull/1298)
 * FIX: Use released Prov python library (https://github.com/nipy/nipype/pull/1279)

nipype/algorithms/tests/test_auto_ErrorMap.py

@@ -0,0 +1,35 @@
+# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
+from ...testing import assert_equal
+from ..metrics import ErrorMap
+
+
+def test_ErrorMap_inputs():
+    input_map = dict(ignore_exception=dict(nohash=True,
+    usedefault=True,
+    ),
+    in_ref=dict(mandatory=True,
+    ),
+    in_tst=dict(mandatory=True,
+    ),
+    mask=dict(),
+    metric=dict(mandatory=True,
+    usedefault=True,
+    ),
+    out_map=dict(),
+    )
+    inputs = ErrorMap.input_spec()
+
+    for key, metadata in list(input_map.items()):
+        for metakey, value in list(metadata.items()):
+            yield assert_equal, getattr(inputs.traits()[key], metakey), value
+
+
+def test_ErrorMap_outputs():
+    output_map = dict(distance=dict(),
+    out_map=dict(),
+    )
+    outputs = ErrorMap.output_spec()
+
+    for key, metadata in list(output_map.items()):
+        for metakey, value in list(metadata.items()):
+            yield assert_equal, getattr(outputs.traits()[key], metakey), value

nipype/algorithms/tests/test_auto_Overlap.py

@@ -0,0 +1,47 @@
+# AUTO-GENERATED by tools/checkspecs.py - DO NOT EDIT
+from ...testing import assert_equal
+from ..misc import Overlap
+
+
+def test_Overlap_inputs():
+    input_map = dict(bg_overlap=dict(mandatory=True,
+    usedefault=True,
+    ),
+    ignore_exception=dict(nohash=True,
+    usedefault=True,
+    ),
+    mask_volume=dict(),
+    out_file=dict(usedefault=True,
+    ),
+    vol_units=dict(mandatory=True,
+    usedefault=True,
+    ),
+    volume1=dict(mandatory=True,
+    ),
+    volume2=dict(mandatory=True,
+    ),
+    weighting=dict(usedefault=True,
+    ),
+    )
+    inputs = Overlap.input_spec()
+
+    for key, metadata in list(input_map.items()):
+        for metakey, value in list(metadata.items()):
+            yield assert_equal, getattr(inputs.traits()[key], metakey), value
+
+
+def test_Overlap_outputs():
+    output_map = dict(dice=dict(),
+    diff_file=dict(),
+    jaccard=dict(),
+    labels=dict(),
+    roi_di=dict(),
+    roi_ji=dict(),
+    roi_voldiff=dict(),
+    volume_difference=dict(),
+    )
+    outputs = Overlap.output_spec()
+
+    for key, metadata in list(output_map.items()):
+        for metakey, value in list(metadata.items()):
+            yield assert_equal, getattr(outputs.traits()[key], metakey), value

nipype/interfaces/semtools/segmentation/specialized.py

@@ -319,6 +319,8 @@ class BRAINSABCInputSpec(CommandLineInputSpec):
     filterMethod = traits.Enum("None", "CurvatureFlow", "GradientAnisotropicDiffusion", "Median", desc="Filter method for preprocessing of registration", argstr="--filterMethod %s")
     maxBiasDegree = traits.Int(desc="Maximum bias degree", argstr="--maxBiasDegree %d")
     useKNN = traits.Bool(desc="Use the KNN stage of estimating posteriors.", argstr="--useKNN ")
+    purePlugsThreshold = traits.Float(desc="If this threshold value is greater than zero, only pure samples are used to compute the distributions in EM classification, and only pure samples are used for KNN training. The default value is set to 0, that means not using pure plugs. However, a value of 0.2 is suggested if you want to activate using pure plugs option.", argstr="--purePlugsThreshold %f")
+    numberOfSubSamplesInEachPlugArea = InputMultiPath(traits.Int, desc="Number of continous index samples taken at each direction of lattice space for each plug volume.", sep=",", argstr="--numberOfSubSamplesInEachPlugArea %s")
     atlasWarpingOff = traits.Bool(desc="Deformable registration of atlas to subject", argstr="--atlasWarpingOff ")
     gridSize = InputMultiPath(traits.Int, desc="Grid size for atlas warping with BSplines", sep=",", argstr="--gridSize %s")
     defaultSuffix = traits.Str(argstr="--defaultSuffix %s")

nipype/interfaces/semtools/segmentation/tests/test_auto_BRAINSABC.py

@@ -54,6 +54,9 @@ def test_BRAINSABC_inputs():
     medianFilterSize=dict(argstr='--medianFilterSize %s',
     sep=',',
     ),
+    numberOfSubSamplesInEachPlugArea=dict(argstr='--numberOfSubSamplesInEachPlugArea %s',
+    sep=',',
+    ),
     numberOfThreads=dict(argstr='--numberOfThreads %d',
     ),
     outputDir=dict(argstr='--outputDir %s',
@@ -72,6 +75,8 @@ def test_BRAINSABC_inputs():
     ),
     posteriorTemplate=dict(argstr='--posteriorTemplate %s',
     ),
+    purePlugsThreshold=dict(argstr='--purePlugsThreshold %f',
+    ),
     restoreState=dict(argstr='--restoreState %s',
     ),
     saveState=dict(argstr='--saveState %s',