Adding niftyseg

nipype/interfaces/niftyseg/__init__.py

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+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+
+"""
+The niftyseg module provides classes for interfacing with the `NIFTYSEG
+<https://sourceforge.net/projects/niftyseg/>`_ command line tools.
+
+Top-level namespace for niftyseg.
+"""
+
+from .base import no_niftyseg, get_custom_path
+from .em import EM
+from .label_fusion import LabelFusion, CalcTopNCC
+from .lesions import FillLesions
+from .maths import (UnaryMaths, BinaryMaths, BinaryMathsInteger, TupleMaths,
+                    Merge)
+from .patchmatch import PatchMatch
+from .stats import UnaryStats, BinaryStats

nipype/interfaces/niftyseg/base.py

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+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+
+"""
+The niftyseg module provides classes for interfacing with `niftyseg
+<https://sourceforge.net/projects/niftyseg/>`_ command line tools.
+
+These are the base tools for working with niftyseg.
+
+EM Statistical Segmentation tool is found in niftyseg/em.py
+Fill lesions tool is found in niftyseg/lesions.py
+Mathematical operation tool is found in niftyseg/maths.py
+Patch Match tool is found in niftyseg/patchmatch.py
+Statistical operation tool is found in niftyseg/stats.py
+Label Fusion and CalcTopNcc tools are in niftyseg/steps.py
+
+Examples
+--------
+See the docstrings of the individual classes for examples.
+
+"""
+
+from nipype.interfaces.base import CommandLine
+from nipype.utils.filemanip import split_filename
+import os
+import subprocess
+import warnings
+
+
+warn = warnings.warn
+warnings.filterwarnings('always', category=UserWarning)
+
+
+def get_custom_path(command):
+    """Get path of niftyseg."""
+    try:
+        specific_dir = os.environ['NIFTYSEGDIR']
+        command = os.path.join(specific_dir, command)
+        return command
+    except KeyError:
+        return command
+
+
+def no_niftyseg(cmd='seg_LabFusion'):
+    """Check if niftyseg is installed."""
+    if True in [os.path.isfile(os.path.join(path, cmd)) and
+                os.access(os.path.join(path, cmd), os.X_OK)
+                for path in os.environ["PATH"].split(os.pathsep)]:
+        return False
+    return True
+
+
+class NiftySegCommand(CommandLine):
+    """
+    Base support interface for NiftySeg commands.
+    """
+    _suffix = '_ns'
+
+    def __init__(self, **inputs):
+        super(NiftySegCommand, self).__init__(**inputs)
+
+    def get_version(self):
+        if no_niftyseg(cmd=self.cmd):
+            return None
+        # exec_cmd = ''.join((self.cmd, ' --version'))
+        exec_cmd = 'seg_EM --version'
+        # Using seg_EM for version (E.G: seg_stats --version doesn't work)
+        return subprocess.check_output(exec_cmd, shell=True).strip('\n')
+
+    @property
+    def version(self):
+        return self.get_version()
+
+    def exists(self):
+        if self.get_version() is None:
+            return False
+        return True
+
+    def _gen_fname(self, basename, out_dir=None, suffix=None, ext=None):
+        if basename == '':
+            msg = 'Unable to generate filename for command %s. ' % self.cmd
+            msg += 'basename is not set!'
+            raise ValueError(msg)
+        _, final_bn, final_ext = split_filename(basename)
+        if out_dir is None:
+            out_dir = os.getcwd()
+        if ext is not None:
+            final_ext = ext
+        if suffix is not None:
+            final_bn = ''.join((final_bn, suffix))
+        return os.path.abspath(os.path.join(out_dir, final_bn + final_ext))

nipype/interfaces/niftyseg/em.py

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+# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
+# vi: set ft=python sts=4 ts=4 sw=4 et:
+
+"""
+Nipype interface for seg_EM.
+
+The em module provides higher-level interfaces to some of the operations
+that can be performed with the seg_em command-line program.
+
+Examples
+--------
+See the docstrings of the individual classes for examples.
+
+Change directory to provide relative paths for doctests
+    >>> import os
+    >>> filepath = os.path.dirname( os.path.realpath( __file__ ) )
+    >>> datadir = os.path.realpath(os.path.join(filepath, '../../testing/data'))
+    >>> os.chdir(datadir)
+"""
+
+from ..base import (TraitedSpec, File, traits, CommandLineInputSpec,
+                    InputMultiPath)
+from .base import NiftySegCommand, get_custom_path
+
+
+class EMInputSpec(CommandLineInputSpec):
+    """Input Spec for EM."""
+    in_file = File(argstr='-in %s',
+                   exists=True,
+                   mandatory=True,
+                   desc='Input image to segment',
+                   position=4)
+
+    mask_file = File(argstr='-mask %s',
+                     exists=True,
+                     desc='Filename of the ROI for label fusion')
+
+    # Priors
+    no_prior = traits.Int(argstr='-nopriors %s',
+                          mandatory=True,
+                          desc='Number of classes to use without prior',
+                          xor=['prior_4D', 'priors'])
+
+    prior_4D = File(argstr='-prior4D %s',
+                    exists=True,
+                    mandatory=True,
+                    desc='4D file containing the priors',
+                    xor=['no_prior', 'priors'])
+
+    desc = 'List of priors filepaths.'
+    priors = InputMultiPath(argstr='%s',
+                            mandatory=True,
+                            desc=desc,
+                            xor=['no_prior', 'prior_4D'])
+
+    # iterations
+    max_iter = traits.Int(argstr='-max_iter %s', default=100,
+                          desc='Maximum number of iterations')
+
+    min_iter = traits.Int(argstr='-min_iter %s', default=0,
+                          desc='Minimun number of iterations')
+
+    # other options
+    bc_order_val = traits.Int(argstr='-bc_order %s', default=3,
+                              desc='Polynomial order for the bias field')
+
+    mrf_beta_val = traits.Float(argstr='-mrf_beta %s',
+                                desc='Weight of the Markov Random Field')
+
+    desc = 'Bias field correction will run only if the ratio of improvement \
+is below bc_thresh. (default=0 [OFF])'
+    bc_thresh_val = traits.Float(argstr='-bc_thresh %s', default=0, desc=desc)
+
+    desc = 'Amount of regularization over the diagonal of the covariance \
+matrix [above 1]'
+    reg_val = traits.Float(argstr='-reg %s', desc=desc)
+
+    desc = 'Outlier detection as in (Van Leemput TMI 2003). <fl1> is the \
+Mahalanobis threshold [recommended between 3 and 7] <fl2> is a convergence \
+ratio below which the outlier detection is going to be done [recommended 0.01]'
+    outlier_val = traits.Tuple(traits.Float(), traits.Float(),
+                               argstr='-outlier %s %s',
+                               desc=desc)
+
+    desc = 'Relax Priors [relaxation factor: 0<rf<1 (recommended=0.5), \
+gaussian regularization: gstd>0 (recommended=2.0)] /only 3D/'
+    relax_priors = traits.Tuple(traits.Float(), traits.Float(),
+                                argstr='-rf %s %s',
+                                desc=desc)
+
+    # outputs
+    out_file = File(name_source=['in_file'],
+                    name_template='%s_em.nii.gz',
+                    argstr='-out %s',
+                    desc='Output segmentation')
+    out_bc_file = File(name_source=['in_file'],
+                       name_template='%s_bc_em.nii.gz',
+                       argstr='-bc_out %s',
+                       desc='Output bias corrected image')
+    out_outlier_file = File(name_source=['in_file'],
+                            name_template='%s_outlier_em.nii.gz',
+                            argstr='-out_outlier %s',
+                            desc='Output outlierness image')
+
+
+class EMOutputSpec(TraitedSpec):
+    """Output Spec for EM."""
+    out_file = File(desc="Output segmentation")
+    out_bc_file = File(desc="Output bias corrected image")
+    out_outlier_file = File(desc='Output outlierness image')
+
+
+class EM(NiftySegCommand):
+    """Interface for executable seg_EM from NiftySeg platform.
+
+    seg_EM is a general purpose intensity based image segmentation tool. In
+    it's simplest form, it takes in one 2D or 3D image and segments it in n
+    classes.
+
+    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
+
+    Examples
+    --------
+    >>> from nipype.interfaces import niftyseg
+    >>> node = niftyseg.EM()
+    >>> node.inputs.in_file = 'im1.nii'
+    >>> node.inputs.no_prior = 4
+    >>> node.cmdline  # doctest: +ALLOW_UNICODE
+    'seg_EM -in im1.nii -nopriors 4 -bc_out im1_bc_em.nii.gz \
+-out im1_em.nii.gz -out_outlier im1_outlier_em.nii.gz'
+
+    """
+    _cmd = get_custom_path('seg_EM')
+    _suffix = '_em'
+    input_spec = EMInputSpec
+    output_spec = EMOutputSpec
+
+    def _format_arg(self, opt, spec, val):
+        """Convert input to appropriate format for seg_EM."""
+        if opt == 'priors':
+            _nb_priors = len(self.inputs.priors)
+            return '-priors %d %s' % (_nb_priors, ' '.join(self.inputs.priors))
+        else:
+            return super(EM, self)._format_arg(opt, spec, val)