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Inital commit to add niftyseg
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‎nipype/interfaces/niftyseg/__init__.py

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

‎nipype/interfaces/niftyseg/em.py

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

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