/
developer.py
1539 lines (1390 loc) · 52.6 KB
/
developer.py
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# -*- coding: utf-8 -*-
# -*- coding: utf8 -*-
"""Autogenerated file - DO NOT EDIT
If you spot a bug, please report it on the mailing list and/or change the generator."""
import os
from ..base import (
CommandLine,
CommandLineInputSpec,
SEMLikeCommandLine,
TraitedSpec,
File,
Directory,
traits,
isdefined,
InputMultiPath,
OutputMultiPath,
)
class JistLaminarVolumetricLayeringInputSpec(CommandLineInputSpec):
inInner = File(
desc="Inner Distance Image (GM/WM boundary)", exists=True, argstr="--inInner %s"
)
inOuter = File(
desc="Outer Distance Image (CSF/GM boundary)",
exists=True,
argstr="--inOuter %s",
)
inNumber = traits.Int(desc="Number of layers", argstr="--inNumber %d")
inMax = traits.Int(
desc="Max iterations for narrow band evolution", argstr="--inMax %d"
)
inMin = traits.Float(
desc="Min change ratio for narrow band evolution", argstr="--inMin %f"
)
inLayering = traits.Enum(
"distance-preserving",
"volume-preserving",
desc="Layering method",
argstr="--inLayering %s",
)
inLayering2 = traits.Enum(
"outward", "inward", desc="Layering direction", argstr="--inLayering2 %s"
)
incurvature = traits.Int(
desc="curvature approximation scale (voxels)", argstr="--incurvature %d"
)
inratio = traits.Float(
desc="ratio smoothing kernel size (voxels)", argstr="--inratio %f"
)
inpresmooth = traits.Enum(
"true", "false", desc="pre-smooth cortical surfaces", argstr="--inpresmooth %s"
)
inTopology = traits.Enum(
"26/6",
"6/26",
"18/6",
"6/18",
"6/6",
"wcs",
"wco",
"no",
desc="Topology",
argstr="--inTopology %s",
)
xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
outContinuous = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Continuous depth measurement",
argstr="--outContinuous %s",
)
outDiscrete = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Discrete sampled layers",
argstr="--outDiscrete %s",
)
outLayer = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Layer boundary surfaces",
argstr="--outLayer %s",
)
null = traits.Str(desc="Execution Time", argstr="--null %s")
xDefaultMem = traits.Int(
desc="Set default maximum heap size", argstr="-xDefaultMem %d"
)
xMaxProcess = traits.Int(
1,
desc="Set default maximum number of processes.",
argstr="-xMaxProcess %d",
usedefault=True,
)
class JistLaminarVolumetricLayeringOutputSpec(TraitedSpec):
outContinuous = File(desc="Continuous depth measurement", exists=True)
outDiscrete = File(desc="Discrete sampled layers", exists=True)
outLayer = File(desc="Layer boundary surfaces", exists=True)
class JistLaminarVolumetricLayering(SEMLikeCommandLine):
"""Volumetric Layering.
Builds a continuous layering of the cortex following distance-preserving or volume-preserving
models of cortical folding.
References
----------
Waehnert MD, Dinse J, Weiss M, Streicher MN, Waehnert P, Geyer S, Turner R, Bazin PL,
Anatomically motivated modeling of cortical laminae, Neuroimage, 2013.
"""
input_spec = JistLaminarVolumetricLayeringInputSpec
output_spec = JistLaminarVolumetricLayeringOutputSpec
_cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.laminar.JistLaminarVolumetricLayering "
_outputs_filenames = {
"outContinuous": "outContinuous.nii",
"outLayer": "outLayer.nii",
"outDiscrete": "outDiscrete.nii",
}
_redirect_x = True
class JistBrainMgdmSegmentationInputSpec(CommandLineInputSpec):
inMP2RAGE = File(desc="MP2RAGE T1 Map Image", exists=True, argstr="--inMP2RAGE %s")
inMP2RAGE2 = File(
desc="MP2RAGE T1-weighted Image", exists=True, argstr="--inMP2RAGE2 %s"
)
inPV = File(desc="PV / Dura Image", exists=True, argstr="--inPV %s")
inMPRAGE = File(
desc="MPRAGE T1-weighted Image", exists=True, argstr="--inMPRAGE %s"
)
inFLAIR = File(desc="FLAIR Image", exists=True, argstr="--inFLAIR %s")
inAtlas = File(desc="Atlas file", exists=True, argstr="--inAtlas %s")
inData = traits.Float(desc="Data weight", argstr="--inData %f")
inCurvature = traits.Float(desc="Curvature weight", argstr="--inCurvature %f")
inPosterior = traits.Float(desc="Posterior scale (mm)", argstr="--inPosterior %f")
inMax = traits.Int(desc="Max iterations", argstr="--inMax %d")
inMin = traits.Float(desc="Min change", argstr="--inMin %f")
inSteps = traits.Int(desc="Steps", argstr="--inSteps %d")
inTopology = traits.Enum(
"26/6",
"6/26",
"18/6",
"6/18",
"6/6",
"wcs",
"wco",
"no",
desc="Topology",
argstr="--inTopology %s",
)
inCompute = traits.Enum(
"true", "false", desc="Compute posteriors", argstr="--inCompute %s"
)
inAdjust = traits.Enum(
"true", "false", desc="Adjust intensity priors", argstr="--inAdjust %s"
)
inOutput = traits.Enum(
"segmentation", "memberships", desc="Output images", argstr="--inOutput %s"
)
xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
outSegmented = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Segmented Brain Image",
argstr="--outSegmented %s",
)
outLevelset = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Levelset Boundary Image",
argstr="--outLevelset %s",
)
outPosterior2 = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Posterior Maximum Memberships (4D)",
argstr="--outPosterior2 %s",
)
outPosterior3 = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Posterior Maximum Labels (4D)",
argstr="--outPosterior3 %s",
)
null = traits.Str(desc="Execution Time", argstr="--null %s")
xDefaultMem = traits.Int(
desc="Set default maximum heap size", argstr="-xDefaultMem %d"
)
xMaxProcess = traits.Int(
1,
desc="Set default maximum number of processes.",
argstr="-xMaxProcess %d",
usedefault=True,
)
class JistBrainMgdmSegmentationOutputSpec(TraitedSpec):
outSegmented = File(desc="Segmented Brain Image", exists=True)
outLevelset = File(desc="Levelset Boundary Image", exists=True)
outPosterior2 = File(desc="Posterior Maximum Memberships (4D)", exists=True)
outPosterior3 = File(desc="Posterior Maximum Labels (4D)", exists=True)
class JistBrainMgdmSegmentation(SEMLikeCommandLine):
"""MGDM Whole Brain Segmentation.
Estimate brain structures from an atlas for a MRI dataset (multiple input combinations
are possible).
"""
input_spec = JistBrainMgdmSegmentationInputSpec
output_spec = JistBrainMgdmSegmentationOutputSpec
_cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.brain.JistBrainMgdmSegmentation "
_outputs_filenames = {
"outSegmented": "outSegmented.nii",
"outPosterior2": "outPosterior2.nii",
"outPosterior3": "outPosterior3.nii",
"outLevelset": "outLevelset.nii",
}
_redirect_x = True
class JistLaminarProfileGeometryInputSpec(CommandLineInputSpec):
inProfile = File(desc="Profile Surface Image", exists=True, argstr="--inProfile %s")
incomputed = traits.Enum(
"thickness",
"curvedness",
"shape_index",
"mean_curvature",
"gauss_curvature",
"profile_length",
"profile_curvature",
"profile_torsion",
desc="computed measure",
argstr="--incomputed %s",
)
inregularization = traits.Enum(
"none", "Gaussian", desc="regularization", argstr="--inregularization %s"
)
insmoothing = traits.Float(desc="smoothing parameter", argstr="--insmoothing %f")
inoutside = traits.Float(desc="outside extension (mm)", argstr="--inoutside %f")
xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
outResult = traits.Either(
traits.Bool, File(), hash_files=False, desc="Result", argstr="--outResult %s"
)
null = traits.Str(desc="Execution Time", argstr="--null %s")
xDefaultMem = traits.Int(
desc="Set default maximum heap size", argstr="-xDefaultMem %d"
)
xMaxProcess = traits.Int(
1,
desc="Set default maximum number of processes.",
argstr="-xMaxProcess %d",
usedefault=True,
)
class JistLaminarProfileGeometryOutputSpec(TraitedSpec):
outResult = File(desc="Result", exists=True)
class JistLaminarProfileGeometry(SEMLikeCommandLine):
"""Compute various geometric quantities for a cortical layers."""
input_spec = JistLaminarProfileGeometryInputSpec
output_spec = JistLaminarProfileGeometryOutputSpec
_cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.laminar.JistLaminarProfileGeometry "
_outputs_filenames = {"outResult": "outResult.nii"}
_redirect_x = True
class JistLaminarProfileCalculatorInputSpec(CommandLineInputSpec):
inIntensity = File(
desc="Intensity Profile Image", exists=True, argstr="--inIntensity %s"
)
inMask = File(desc="Mask Image (opt, 3D or 4D)", exists=True, argstr="--inMask %s")
incomputed = traits.Enum(
"mean",
"stdev",
"skewness",
"kurtosis",
desc="computed statistic",
argstr="--incomputed %s",
)
xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
outResult = traits.Either(
traits.Bool, File(), hash_files=False, desc="Result", argstr="--outResult %s"
)
null = traits.Str(desc="Execution Time", argstr="--null %s")
xDefaultMem = traits.Int(
desc="Set default maximum heap size", argstr="-xDefaultMem %d"
)
xMaxProcess = traits.Int(
1,
desc="Set default maximum number of processes.",
argstr="-xMaxProcess %d",
usedefault=True,
)
class JistLaminarProfileCalculatorOutputSpec(TraitedSpec):
outResult = File(desc="Result", exists=True)
class JistLaminarProfileCalculator(SEMLikeCommandLine):
"""Compute various moments for intensities mapped along a cortical profile."""
input_spec = JistLaminarProfileCalculatorInputSpec
output_spec = JistLaminarProfileCalculatorOutputSpec
_cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.laminar.JistLaminarProfileCalculator "
_outputs_filenames = {"outResult": "outResult.nii"}
_redirect_x = True
class MedicAlgorithmN3InputSpec(CommandLineInputSpec):
inInput = File(desc="Input Volume", exists=True, argstr="--inInput %s")
inSignal = traits.Float(
desc="Default = min + 1, Values at less than threshold are treated as part of the background",
argstr="--inSignal %f",
)
inMaximum = traits.Int(desc="Maximum number of Iterations", argstr="--inMaximum %d")
inEnd = traits.Float(
desc="Usually 0.01-0.00001, The measure used to terminate the iterations is the coefficient of variation of change in field estimates between successive iterations.",
argstr="--inEnd %f",
)
inField = traits.Float(
desc="Characteristic distance over which the field varies. The distance between adjacent knots in bspline fitting with at least 4 knots going in every dimension. The default in the dialog is one third the distance (resolution * extents) of the smallest dimension.",
argstr="--inField %f",
)
inSubsample = traits.Float(
desc="Usually between 1-32, The factor by which the data is subsampled to a lower resolution in estimating the slowly varying non-uniformity field. Reduce sampling in the finest sampling direction by the shrink factor.",
argstr="--inSubsample %f",
)
inKernel = traits.Float(
desc="Usually between 0.05-0.50, Width of deconvolution kernel used to sharpen the histogram. Larger values give faster convergence while smaller values give greater accuracy.",
argstr="--inKernel %f",
)
inWeiner = traits.Float(desc="Usually between 0.0-1.0", argstr="--inWeiner %f")
inAutomatic = traits.Enum(
"true",
"false",
desc="If true determines the threshold by histogram analysis. If true a VOI cannot be used and the input threshold is ignored.",
argstr="--inAutomatic %s",
)
xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
outInhomogeneity = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Inhomogeneity Corrected Volume",
argstr="--outInhomogeneity %s",
)
outInhomogeneity2 = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Inhomogeneity Field",
argstr="--outInhomogeneity2 %s",
)
null = traits.Str(desc="Execution Time", argstr="--null %s")
xDefaultMem = traits.Int(
desc="Set default maximum heap size", argstr="-xDefaultMem %d"
)
xMaxProcess = traits.Int(
1,
desc="Set default maximum number of processes.",
argstr="-xMaxProcess %d",
usedefault=True,
)
class MedicAlgorithmN3OutputSpec(TraitedSpec):
outInhomogeneity = File(desc="Inhomogeneity Corrected Volume", exists=True)
outInhomogeneity2 = File(desc="Inhomogeneity Field", exists=True)
class MedicAlgorithmN3(SEMLikeCommandLine):
"""Non-parametric Intensity Non-uniformity Correction, N3, originally by J.G. Sled."""
input_spec = MedicAlgorithmN3InputSpec
output_spec = MedicAlgorithmN3OutputSpec
_cmd = "java edu.jhu.ece.iacl.jist.cli.run edu.jhu.ece.iacl.plugins.classification.MedicAlgorithmN3 "
_outputs_filenames = {
"outInhomogeneity2": "outInhomogeneity2.nii",
"outInhomogeneity": "outInhomogeneity.nii",
}
_redirect_x = True
class JistLaminarROIAveragingInputSpec(CommandLineInputSpec):
inIntensity = File(
desc="Intensity Profile Image", exists=True, argstr="--inIntensity %s"
)
inROI = File(desc="ROI Mask", exists=True, argstr="--inROI %s")
inROI2 = traits.Str(desc="ROI Name", argstr="--inROI2 %s")
inMask = File(desc="Mask Image (opt, 3D or 4D)", exists=True, argstr="--inMask %s")
xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
outROI3 = traits.Either(
traits.Bool, File(), hash_files=False, desc="ROI Average", argstr="--outROI3 %s"
)
null = traits.Str(desc="Execution Time", argstr="--null %s")
xDefaultMem = traits.Int(
desc="Set default maximum heap size", argstr="-xDefaultMem %d"
)
xMaxProcess = traits.Int(
1,
desc="Set default maximum number of processes.",
argstr="-xMaxProcess %d",
usedefault=True,
)
class JistLaminarROIAveragingOutputSpec(TraitedSpec):
outROI3 = File(desc="ROI Average", exists=True)
class JistLaminarROIAveraging(SEMLikeCommandLine):
"""Compute an average profile over a given ROI."""
input_spec = JistLaminarROIAveragingInputSpec
output_spec = JistLaminarROIAveragingOutputSpec
_cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.laminar.JistLaminarROIAveraging "
_outputs_filenames = {"outROI3": "outROI3"}
_redirect_x = True
class MedicAlgorithmLesionToadsInputSpec(CommandLineInputSpec):
inT1_MPRAGE = File(desc="T1_MPRAGE Image", exists=True, argstr="--inT1_MPRAGE %s")
inT1_SPGR = File(desc="T1_SPGR Image", exists=True, argstr="--inT1_SPGR %s")
inFLAIR = File(desc="FLAIR Image", exists=True, argstr="--inFLAIR %s")
inAtlas = traits.Enum(
"With Lesion", "No Lesion", desc="Atlas to Use", argstr="--inAtlas %s"
)
inOutput = traits.Enum(
"hard segmentation",
"hard segmentation+memberships",
"cruise inputs",
"dura removal inputs",
desc="Output images",
argstr="--inOutput %s",
)
inOutput2 = traits.Enum(
"true",
"false",
desc="Output the hard classification using maximum membership (not neceesarily topologically correct)",
argstr="--inOutput2 %s",
)
inCorrect = traits.Enum(
"true", "false", desc="Correct MR field inhomogeneity.", argstr="--inCorrect %s"
)
inOutput3 = traits.Enum(
"true",
"false",
desc="Output the estimated inhomogeneity field",
argstr="--inOutput3 %s",
)
inAtlas2 = File(
desc="Atlas File - With Lesions", exists=True, argstr="--inAtlas2 %s"
)
inAtlas3 = File(
desc="Atlas File - No Lesion - T1 and FLAIR",
exists=True,
argstr="--inAtlas3 %s",
)
inAtlas4 = File(
desc="Atlas File - No Lesion - T1 Only", exists=True, argstr="--inAtlas4 %s"
)
inMaximum = traits.Int(
desc="Maximum distance from the interventricular WM boundary to downweight the lesion membership to avoid false postives",
argstr="--inMaximum %d",
)
inMaximum2 = traits.Int(desc="Maximum Ventircle Distance", argstr="--inMaximum2 %d")
inMaximum3 = traits.Int(
desc="Maximum InterVentricular Distance", argstr="--inMaximum3 %d"
)
inInclude = traits.Enum(
"true",
"false",
desc="Include lesion in WM class in hard classification",
argstr="--inInclude %s",
)
inAtlas5 = traits.Float(
desc="Controls the effect of the statistical atlas on the segmentation",
argstr="--inAtlas5 %f",
)
inSmooting = traits.Float(
desc="Controls the effect of neighberhood voxels on the membership",
argstr="--inSmooting %f",
)
inMaximum4 = traits.Float(
desc="Maximum amount of relative change in the energy function considered as the convergence criteria",
argstr="--inMaximum4 %f",
)
inMaximum5 = traits.Int(desc="Maximum iterations", argstr="--inMaximum5 %d")
inAtlas6 = traits.Enum(
"rigid", "multi_fully_affine", desc="Atlas alignment", argstr="--inAtlas6 %s"
)
inConnectivity = traits.Enum(
"(26,6)",
"(6,26)",
"(6,18)",
"(18,6)",
desc="Connectivity (foreground,background)",
argstr="--inConnectivity %s",
)
xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
outHard = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Hard segmentation",
argstr="--outHard %s",
)
outHard2 = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Hard segmentationfrom memberships",
argstr="--outHard2 %s",
)
outInhomogeneity = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Inhomogeneity Field",
argstr="--outInhomogeneity %s",
)
outMembership = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Membership Functions",
argstr="--outMembership %s",
)
outLesion = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Lesion Segmentation",
argstr="--outLesion %s",
)
outSulcal = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Sulcal CSF Membership",
argstr="--outSulcal %s",
)
outCortical = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Cortical GM Membership",
argstr="--outCortical %s",
)
outFilled = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Filled WM Membership",
argstr="--outFilled %s",
)
outWM = traits.Either(
traits.Bool, File(), hash_files=False, desc="WM Mask", argstr="--outWM %s"
)
null = traits.Str(desc="Execution Time", argstr="--null %s")
xDefaultMem = traits.Int(
desc="Set default maximum heap size", argstr="-xDefaultMem %d"
)
xMaxProcess = traits.Int(
1,
desc="Set default maximum number of processes.",
argstr="-xMaxProcess %d",
usedefault=True,
)
class MedicAlgorithmLesionToadsOutputSpec(TraitedSpec):
outHard = File(desc="Hard segmentation", exists=True)
outHard2 = File(desc="Hard segmentationfrom memberships", exists=True)
outInhomogeneity = File(desc="Inhomogeneity Field", exists=True)
outMembership = File(desc="Membership Functions", exists=True)
outLesion = File(desc="Lesion Segmentation", exists=True)
outSulcal = File(desc="Sulcal CSF Membership", exists=True)
outCortical = File(desc="Cortical GM Membership", exists=True)
outFilled = File(desc="Filled WM Membership", exists=True)
outWM = File(desc="WM Mask", exists=True)
class MedicAlgorithmLesionToads(SEMLikeCommandLine):
"""Algorithm for simulataneous brain structures and MS lesion segmentation of MS Brains.
The brain segmentation is topologically consistent and the algorithm can use multiple
MR sequences as input data.
References
----------
N. Shiee, P.-L. Bazin, A.Z. Ozturk, P.A. Calabresi, D.S. Reich, D.L. Pham,
"A Topology-Preserving Approach to the Segmentation of Brain Images with Multiple Sclerosis",
NeuroImage, vol. 49, no. 2, pp. 1524-1535, 2010.
"""
input_spec = MedicAlgorithmLesionToadsInputSpec
output_spec = MedicAlgorithmLesionToadsOutputSpec
_cmd = "java edu.jhu.ece.iacl.jist.cli.run edu.jhu.ece.iacl.plugins.classification.MedicAlgorithmLesionToads "
_outputs_filenames = {
"outWM": "outWM.nii",
"outHard": "outHard.nii",
"outFilled": "outFilled.nii",
"outMembership": "outMembership.nii",
"outInhomogeneity": "outInhomogeneity.nii",
"outCortical": "outCortical.nii",
"outHard2": "outHard2.nii",
"outLesion": "outLesion.nii",
"outSulcal": "outSulcal.nii",
}
_redirect_x = True
class JistBrainMp2rageSkullStrippingInputSpec(CommandLineInputSpec):
inSecond = File(
desc="Second inversion (Inv2) Image", exists=True, argstr="--inSecond %s"
)
inT1 = File(desc="T1 Map (T1_Images) Image (opt)", exists=True, argstr="--inT1 %s")
inT1weighted = File(
desc="T1-weighted (UNI) Image (opt)", exists=True, argstr="--inT1weighted %s"
)
inFilter = File(desc="Filter Image (opt)", exists=True, argstr="--inFilter %s")
inSkip = traits.Enum("true", "false", desc="Skip zero values", argstr="--inSkip %s")
xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
outBrain = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Brain Mask Image",
argstr="--outBrain %s",
)
outMasked = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Masked T1 Map Image",
argstr="--outMasked %s",
)
outMasked2 = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Masked T1-weighted Image",
argstr="--outMasked2 %s",
)
outMasked3 = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Masked Filter Image",
argstr="--outMasked3 %s",
)
null = traits.Str(desc="Execution Time", argstr="--null %s")
xDefaultMem = traits.Int(
desc="Set default maximum heap size", argstr="-xDefaultMem %d"
)
xMaxProcess = traits.Int(
1,
desc="Set default maximum number of processes.",
argstr="-xMaxProcess %d",
usedefault=True,
)
class JistBrainMp2rageSkullStrippingOutputSpec(TraitedSpec):
outBrain = File(desc="Brain Mask Image", exists=True)
outMasked = File(desc="Masked T1 Map Image", exists=True)
outMasked2 = File(desc="Masked T1-weighted Image", exists=True)
outMasked3 = File(desc="Masked Filter Image", exists=True)
class JistBrainMp2rageSkullStripping(SEMLikeCommandLine):
"""Estimate a brain mask for a MP2RAGE dataset.
At least a T1-weighted or a T1 map image is required.
"""
input_spec = JistBrainMp2rageSkullStrippingInputSpec
output_spec = JistBrainMp2rageSkullStrippingOutputSpec
_cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.brain.JistBrainMp2rageSkullStripping "
_outputs_filenames = {
"outBrain": "outBrain.nii",
"outMasked3": "outMasked3.nii",
"outMasked2": "outMasked2.nii",
"outMasked": "outMasked.nii",
}
_redirect_x = True
class JistCortexSurfaceMeshInflationInputSpec(CommandLineInputSpec):
inLevelset = File(desc="Levelset Image", exists=True, argstr="--inLevelset %s")
inSOR = traits.Float(desc="SOR Parameter", argstr="--inSOR %f")
inMean = traits.Float(desc="Mean Curvature Threshold", argstr="--inMean %f")
inStep = traits.Int(desc="Step Size", argstr="--inStep %d")
inMax = traits.Int(desc="Max Iterations", argstr="--inMax %d")
inLorentzian = traits.Enum(
"true", "false", desc="Lorentzian Norm", argstr="--inLorentzian %s"
)
inTopology = traits.Enum(
"26/6",
"6/26",
"18/6",
"6/18",
"6/6",
"wcs",
"wco",
"no",
desc="Topology",
argstr="--inTopology %s",
)
xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
outOriginal = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Original Surface",
argstr="--outOriginal %s",
)
outInflated = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Inflated Surface",
argstr="--outInflated %s",
)
null = traits.Str(desc="Execution Time", argstr="--null %s")
xDefaultMem = traits.Int(
desc="Set default maximum heap size", argstr="-xDefaultMem %d"
)
xMaxProcess = traits.Int(
1,
desc="Set default maximum number of processes.",
argstr="-xMaxProcess %d",
usedefault=True,
)
class JistCortexSurfaceMeshInflationOutputSpec(TraitedSpec):
outOriginal = File(desc="Original Surface", exists=True)
outInflated = File(desc="Inflated Surface", exists=True)
class JistCortexSurfaceMeshInflation(SEMLikeCommandLine):
"""Inflates a cortical surface mesh.
References
----------
D. Tosun, M. E. Rettmann, X. Han, X. Tao, C. Xu, S. M. Resnick, D. Pham, and J. L. Prince,
Cortical Surface Segmentation and Mapping, NeuroImage, vol. 23, pp. S108--S118, 2004.
"""
input_spec = JistCortexSurfaceMeshInflationInputSpec
output_spec = JistCortexSurfaceMeshInflationOutputSpec
_cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.cortex.JistCortexSurfaceMeshInflation "
_outputs_filenames = {"outOriginal": "outOriginal", "outInflated": "outInflated"}
_redirect_x = True
class RandomVolInputSpec(CommandLineInputSpec):
inSize = traits.Int(desc="Size of Volume in X direction", argstr="--inSize %d")
inSize2 = traits.Int(desc="Size of Volume in Y direction", argstr="--inSize2 %d")
inSize3 = traits.Int(desc="Size of Volume in Z direction", argstr="--inSize3 %d")
inSize4 = traits.Int(desc="Size of Volume in t direction", argstr="--inSize4 %d")
inStandard = traits.Int(
desc="Standard Deviation for Normal Distribution", argstr="--inStandard %d"
)
inLambda = traits.Float(
desc="Lambda Value for Exponential Distribution", argstr="--inLambda %f"
)
inMaximum = traits.Int(desc="Maximum Value", argstr="--inMaximum %d")
inMinimum = traits.Int(desc="Minimum Value", argstr="--inMinimum %d")
inField = traits.Enum(
"Uniform", "Normal", "Exponential", desc="Field", argstr="--inField %s"
)
xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
outRand1 = traits.Either(
traits.Bool, File(), hash_files=False, desc="Rand1", argstr="--outRand1 %s"
)
null = traits.Str(desc="Execution Time", argstr="--null %s")
xDefaultMem = traits.Int(
desc="Set default maximum heap size", argstr="-xDefaultMem %d"
)
xMaxProcess = traits.Int(
1,
desc="Set default maximum number of processes.",
argstr="-xMaxProcess %d",
usedefault=True,
)
class RandomVolOutputSpec(TraitedSpec):
outRand1 = File(desc="Rand1", exists=True)
class RandomVol(SEMLikeCommandLine):
"""Generate a volume of random scalars."""
input_spec = RandomVolInputSpec
output_spec = RandomVolOutputSpec
_cmd = "java edu.jhu.ece.iacl.jist.cli.run edu.jhu.bme.smile.demo.RandomVol "
_outputs_filenames = {"outRand1": "outRand1.nii"}
_redirect_x = True
class MedicAlgorithmImageCalculatorInputSpec(CommandLineInputSpec):
inVolume = File(desc="Volume 1", exists=True, argstr="--inVolume %s")
inVolume2 = File(desc="Volume 2", exists=True, argstr="--inVolume2 %s")
inOperation = traits.Enum(
"Add",
"Subtract",
"Multiply",
"Divide",
"Min",
"Max",
desc="Operation",
argstr="--inOperation %s",
)
xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
outResult = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Result Volume",
argstr="--outResult %s",
)
null = traits.Str(desc="Execution Time", argstr="--null %s")
xDefaultMem = traits.Int(
desc="Set default maximum heap size", argstr="-xDefaultMem %d"
)
xMaxProcess = traits.Int(
1,
desc="Set default maximum number of processes.",
argstr="-xMaxProcess %d",
usedefault=True,
)
class MedicAlgorithmImageCalculatorOutputSpec(TraitedSpec):
outResult = File(desc="Result Volume", exists=True)
class MedicAlgorithmImageCalculator(SEMLikeCommandLine):
"""Perform simple image calculator operations on two images.
The operations include 'Add', 'Subtract', 'Multiply', and 'Divide'
"""
input_spec = MedicAlgorithmImageCalculatorInputSpec
output_spec = MedicAlgorithmImageCalculatorOutputSpec
_cmd = "java edu.jhu.ece.iacl.jist.cli.run edu.jhu.ece.iacl.plugins.utilities.math.MedicAlgorithmImageCalculator "
_outputs_filenames = {"outResult": "outResult.nii"}
_redirect_x = True
class JistBrainMp2rageDuraEstimationInputSpec(CommandLineInputSpec):
inSecond = File(
desc="Second inversion (Inv2) Image", exists=True, argstr="--inSecond %s"
)
inSkull = File(desc="Skull Stripping Mask", exists=True, argstr="--inSkull %s")
inDistance = traits.Float(
desc="Distance to background (mm)", argstr="--inDistance %f"
)
inoutput = traits.Enum(
"dura_region",
"boundary",
"dura_prior",
"bg_prior",
"intens_prior",
desc="Outputs an estimate of the dura / CSF boundary or an estimate of the entire dura region.",
argstr="--inoutput %s",
)
xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
outDura = traits.Either(
traits.Bool, File(), hash_files=False, desc="Dura Image", argstr="--outDura %s"
)
null = traits.Str(desc="Execution Time", argstr="--null %s")
xDefaultMem = traits.Int(
desc="Set default maximum heap size", argstr="-xDefaultMem %d"
)
xMaxProcess = traits.Int(
1,
desc="Set default maximum number of processes.",
argstr="-xMaxProcess %d",
usedefault=True,
)
class JistBrainMp2rageDuraEstimationOutputSpec(TraitedSpec):
outDura = File(desc="Dura Image", exists=True)
class JistBrainMp2rageDuraEstimation(SEMLikeCommandLine):
"""Filters a MP2RAGE brain image to obtain a probability map of dura matter."""
input_spec = JistBrainMp2rageDuraEstimationInputSpec
output_spec = JistBrainMp2rageDuraEstimationOutputSpec
_cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.brain.JistBrainMp2rageDuraEstimation "
_outputs_filenames = {"outDura": "outDura.nii"}
_redirect_x = True
class JistLaminarProfileSamplingInputSpec(CommandLineInputSpec):
inProfile = File(desc="Profile Surface Image", exists=True, argstr="--inProfile %s")
inIntensity = File(desc="Intensity Image", exists=True, argstr="--inIntensity %s")
inCortex = File(desc="Cortex Mask (opt)", exists=True, argstr="--inCortex %s")
xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
outProfilemapped = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Profile-mapped Intensity Image",
argstr="--outProfilemapped %s",
)
outProfile2 = traits.Either(
traits.Bool,
File(),
hash_files=False,
desc="Profile 4D Mask",
argstr="--outProfile2 %s",
)
null = traits.Str(desc="Execution Time", argstr="--null %s")
xDefaultMem = traits.Int(
desc="Set default maximum heap size", argstr="-xDefaultMem %d"
)
xMaxProcess = traits.Int(
1,
desc="Set default maximum number of processes.",
argstr="-xMaxProcess %d",
usedefault=True,
)
class JistLaminarProfileSamplingOutputSpec(TraitedSpec):
outProfilemapped = File(desc="Profile-mapped Intensity Image", exists=True)
outProfile2 = File(desc="Profile 4D Mask", exists=True)
class JistLaminarProfileSampling(SEMLikeCommandLine):
"""Sample some intensity image along a cortical profile across layer surfaces."""
input_spec = JistLaminarProfileSamplingInputSpec
output_spec = JistLaminarProfileSamplingOutputSpec
_cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.laminar.JistLaminarProfileSampling "
_outputs_filenames = {
"outProfile2": "outProfile2.nii",
"outProfilemapped": "outProfilemapped.nii",
}
_redirect_x = True
class MedicAlgorithmMipavReorientInputSpec(CommandLineInputSpec):
inSource = InputMultiPath(File, desc="Source", sep=";", argstr="--inSource %s")
inTemplate = File(desc="Template", exists=True, argstr="--inTemplate %s")
inNew = traits.Enum(
"Dicom axial",
"Dicom coronal",
"Dicom sagittal",
"User defined",
desc="New image orientation",
argstr="--inNew %s",
)
inUser = traits.Enum(
"Unknown",
"Patient Right to Left",
"Patient Left to Right",
"Patient Posterior to Anterior",
"Patient Anterior to Posterior",
"Patient Inferior to Superior",
"Patient Superior to Inferior",
desc="User defined X-axis orientation (image left to right)",
argstr="--inUser %s",
)
inUser2 = traits.Enum(
"Unknown",
"Patient Right to Left",