adding documentation in docstring for segmaths, segstats, seglabfusion

Author: byvernault

nipype/interfaces/niftyseg/label_fusion.py

@@ -114,6 +114,23 @@ class LabelFusion(NiftySegCommand):
     """Interface for executable seg_LabelFusion from NiftySeg platform using
     type STEPS as classifier Fusion.
 
+    This executable implements 4 fusion strategies (-STEPS, -STAPLE, -MV or
+    - SBA), all of them using either a global (-GNCC), ROI-based (-ROINCC),
+    local (-LNCC) or no image similarity (-ALL). Combinations of fusion
+    algorithms and similarity metrics give rise to different variants of known
+    algorithms. As an example, using LNCC and MV as options will run a locally
+    weighted voting strategy with LNCC derived weights, while using STAPLE and
+    LNCC is equivalent to running STEPS as per its original formulation.
+    A few other options pertaining the use of an MRF (-MRF beta), the initial
+    sensitivity and specificity estimates and the use of only non-consensus
+    voxels (-unc) for the STAPLE and STEPS algorithm. All processing can be
+    masked (-mask), greatly reducing memory consumption.
+
+    As an example, the command to use STEPS should be:
+    seg_LabFusion -in 4D_Propragated_Labels_to_fuse.nii -out \
+    FusedSegmentation.nii -STEPS 2 15 TargetImage.nii \
+    4D_Propagated_Intensities.nii
+
     For source code, see http://cmictig.cs.ucl.ac.uk/wiki/index.php/NiftySeg
     For Documentation, see:
         http://cmictig.cs.ucl.ac.uk/wiki/index.php/NiftySeg_documentation

nipype/interfaces/niftyseg/maths.py

@@ -54,6 +54,19 @@ class MathsOutput(TraitedSpec):
 class MathsCommand(NiftySegCommand):
     """
     Base Command Interface for seg_maths interfaces.
+
+    The executable seg_maths enables the sequential execution of arithmetic
+    operations, like multiplication (-mul), division (-div) or addition
+    (-add), binarisation (-bin) or thresholding (-thr) operations and
+    convolution by a Gaussian kernel (-smo). It also alows mathematical
+    morphology based operations like dilation (-dil), erosion (-ero),
+    connected components (-lconcomp) and hole filling (-fill), Euclidean
+    (- euc) and geodesic (-geo) distance transforms, local image similarity
+    metric calculation (-lncc and -lssd). Finally, it allows multiple
+    operations over the dimensionality of the image, from merging 3D images
+    together as a 4D image (-merge) or splitting (-split or -tp) 4D images
+    into several 3D images, to estimating the maximum, minimum and average
+    over all time-points, etc.
     """
     _cmd = get_custom_path('seg_maths')
     input_spec = MathsInput

nipype/interfaces/niftyseg/stats.py

@@ -48,6 +48,16 @@ class StatsOutput(TraitedSpec):
 class StatsCommand(NiftySegCommand):
     """
     Base Command Interface for seg_stats interfaces.
+
+    The executable seg_stats enables the estimation of image statistics on
+    continuous voxel intensities (average, standard deviation, min/max, robust
+    range, percentiles, sum, probabilistic volume, entropy, etc) either over
+    the full image or on a per slice basis (slice axis can be specified),
+    statistics over voxel coordinates (location of max, min and centre of
+    mass, bounding box, etc) and statistics over categorical images (e.g. per
+    region volume, count, average, Dice scores, etc). These statistics are
+    robust to the presence of NaNs, and can be constrained by a mask and/or
+    thresholded at a certain level.
     """
     _cmd = get_custom_path('seg_stats')
     input_spec = StatsInput