# -*- 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", "Patient Left to Right", "Patient Posterior to Anterior", "Patient Anterior to Posterior", "Patient Inferior to Superior", "Patient Superior to Inferior", desc="User defined Y-axis orientation (image top to bottom)", argstr="--inUser2 %s", ) inUser3 = 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 Z-axis orientation (into the screen)", argstr="--inUser3 %s", ) inUser4 = traits.Enum( "Axial", "Coronal", "Sagittal", "Unknown", desc="User defined Image Orientation", argstr="--inUser4 %s", ) inInterpolation = traits.Enum( "Nearest Neighbor", "Trilinear", "Bspline 3rd order", "Bspline 4th order", "Cubic Lagrangian", "Quintic Lagrangian", "Heptic Lagrangian", "Windowed Sinc", desc="Interpolation", argstr="--inInterpolation %s", ) inResolution = traits.Enum( "Unchanged", "Finest cubic", "Coarsest cubic", "Same as template", desc="Resolution", argstr="--inResolution %s", ) xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s") outReoriented = InputMultiPath( File, desc="Reoriented Volume", sep=";", argstr="--outReoriented %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 MedicAlgorithmMipavReorientOutputSpec(TraitedSpec): pass class MedicAlgorithmMipavReorient(SEMLikeCommandLine): """Reorient a volume to a particular anatomical orientation.""" input_spec = MedicAlgorithmMipavReorientInputSpec output_spec = MedicAlgorithmMipavReorientOutputSpec _cmd = "java edu.jhu.ece.iacl.jist.cli.run edu.jhu.ece.iacl.plugins.utilities.volume.MedicAlgorithmMipavReorient " _outputs_filenames = {} _redirect_x = True class MedicAlgorithmSPECTRE2010InputSpec(CommandLineInputSpec): inInput = File( desc="Input volume to be skullstripped.", exists=True, argstr="--inInput %s" ) inAtlas = File( desc="SPECTRE atlas description file. A text file enumerating atlas files and landmarks.", exists=True, argstr="--inAtlas %s", ) inInitial = traits.Int( desc="Erosion of the inital mask, which is based on the probability mask and the classification., The initial mask is ouput as the d0 volume at the conclusion of SPECTRE.", argstr="--inInitial %d", ) inImage = traits.Enum( "T1_SPGR", "T1_ALT", "T1_MPRAGE", "T2", "FLAIR", desc="Set the image modality. MP-RAGE is recommended for most T1 sequence images.", argstr="--inImage %s", ) inOutput = traits.Enum( "true", "false", desc="Determines if the output results are transformed back into the space of the original input image.", argstr="--inOutput %s", ) inFind = traits.Enum( "true", "false", desc="Find Midsaggital Plane", argstr="--inFind %s" ) inRun = traits.Enum( "true", "false", desc="Run Smooth Brain Mask", argstr="--inRun %s" ) inResample = traits.Enum( "true", "false", desc="Determines if the data is resampled to be isotropic during the processing.", argstr="--inResample %s", ) inInitial2 = traits.Float( desc="Initial probability threshold", argstr="--inInitial2 %f" ) inMinimum = traits.Float( desc="Minimum probability threshold", argstr="--inMinimum %f" ) inMMC = traits.Int( desc="The size of the dilation step within the Modified Morphological Closing.", argstr="--inMMC %d", ) inMMC2 = traits.Int( desc="The size of the erosion step within the Modified Morphological Closing.", argstr="--inMMC2 %d", ) inInhomogeneity = traits.Enum( "true", "false", desc="Set to false by default, this parameter will make FANTASM try to do inhomogeneity correction during it's iterative cycle.", argstr="--inInhomogeneity %s", ) inSmoothing = traits.Float(argstr="--inSmoothing %f") inBackground = traits.Float(argstr="--inBackground %f") inOutput2 = traits.Enum( "true", "false", desc="Output Plane?", argstr="--inOutput2 %s" ) inOutput3 = traits.Enum( "true", "false", desc="Output Split-Halves?", argstr="--inOutput3 %s" ) inOutput4 = traits.Enum( "true", "false", desc="Output Segmentation on Plane?", argstr="--inOutput4 %s" ) inDegrees = traits.Enum( "Rigid - 6", "Global rescale - 7", "Specific rescale - 9", "Affine - 12", desc="Degrees of freedom", argstr="--inDegrees %s", ) inCost = traits.Enum( "Correlation ratio", "Least squares", "Normalized cross correlation", "Normalized mutual information", desc="Cost function", argstr="--inCost %s", ) inRegistration = traits.Enum( "Trilinear", "Bspline 3rd order", "Bspline 4th order", "Cubic Lagrangian", "Quintic Lagrangian", "Heptic Lagrangian", "Windowed sinc", desc="Registration interpolation", argstr="--inRegistration %s", ) inOutput5 = traits.Enum( "Trilinear", "Bspline 3rd order", "Bspline 4th order", "Cubic Lagrangian", "Quintic Lagrangian", "Heptic Lagrangian", "Windowed sinc", "Nearest Neighbor", desc="Output interpolation", argstr="--inOutput5 %s", ) inApply = traits.Enum( "All", "X", "Y", "Z", desc="Apply rotation", argstr="--inApply %s" ) inMinimum2 = traits.Float(desc="Minimum angle", argstr="--inMinimum2 %f") inMaximum = traits.Float(desc="Maximum angle", argstr="--inMaximum %f") inCoarse = traits.Float(desc="Coarse angle increment", argstr="--inCoarse %f") inFine = traits.Float(desc="Fine angle increment", argstr="--inFine %f") inMultiple = traits.Int( desc="Multiple of tolerance to bracket the minimum", argstr="--inMultiple %d" ) inNumber = traits.Int(desc="Number of iterations", argstr="--inNumber %d") inNumber2 = traits.Int( desc="Number of minima from Level 8 to test at Level 4", argstr="--inNumber2 %d" ) inUse = traits.Enum( "true", "false", desc="Use the max of the min resolutions of the two datasets when resampling", argstr="--inUse %s", ) inSubsample = traits.Enum( "true", "false", desc="Subsample image for speed", argstr="--inSubsample %s" ) inSkip = traits.Enum( "true", "false", desc="Skip multilevel search (Assume images are close to alignment)", argstr="--inSkip %s", ) inMultithreading = traits.Enum( "true", "false", desc="Set to false by default, this parameter controls the multithreaded behavior of the linear registration.", argstr="--inMultithreading %s", ) xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s") outOriginal = traits.Either( traits.Bool, File(), hash_files=False, desc="If Output in Original Space Flag is true then outputs the original input volume. Otherwise outputs the axialy reoriented input volume.", argstr="--outOriginal %s", ) outStripped = traits.Either( traits.Bool, File(), hash_files=False, desc="Skullstripped result of the input volume with just the brain.", argstr="--outStripped %s", ) outMask = traits.Either( traits.Bool, File(), hash_files=False, desc="Binary Mask of the skullstripped result with just the brain", argstr="--outMask %s", ) outPrior = traits.Either( traits.Bool, File(), hash_files=False, desc="Probability prior from the atlas registrations", argstr="--outPrior %s", ) outFANTASM = traits.Either( traits.Bool, File(), hash_files=False, desc="Tissue classification of of the whole input volume.", argstr="--outFANTASM %s", ) outd0 = traits.Either( traits.Bool, File(), hash_files=False, desc="Initial Brainmask", argstr="--outd0 %s", ) outMidsagittal = traits.Either( traits.Bool, File(), hash_files=False, desc="Plane dividing the brain hemispheres", argstr="--outMidsagittal %s", ) outSplitHalves = traits.Either( traits.Bool, File(), hash_files=False, desc="Skullstripped mask of the brain with the hemispheres divided.", argstr="--outSplitHalves %s", ) outSegmentation = traits.Either( traits.Bool, File(), hash_files=False, desc="2D image showing the tissue classification on the midsagittal plane", argstr="--outSegmentation %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 MedicAlgorithmSPECTRE2010OutputSpec(TraitedSpec): outOriginal = File( desc="If Output in Original Space Flag is true then outputs the original input volume. Otherwise outputs the axialy reoriented input volume.", exists=True, ) outStripped = File( desc="Skullstripped result of the input volume with just the brain.", exists=True, ) outMask = File( desc="Binary Mask of the skullstripped result with just the brain", exists=True ) outPrior = File(desc="Probability prior from the atlas registrations", exists=True) outFANTASM = File( desc="Tissue classification of of the whole input volume.", exists=True ) outd0 = File(desc="Initial Brainmask", exists=True) outMidsagittal = File(desc="Plane dividing the brain hemispheres", exists=True) outSplitHalves = File( desc="Skullstripped mask of the brain with the hemispheres divided.", exists=True, ) outSegmentation = File( desc="2D image showing the tissue classification on the midsagittal plane", exists=True, ) class MedicAlgorithmSPECTRE2010(SEMLikeCommandLine): """SPECTRE 2010: Simple Paradigm for Extra-Cranial Tissue REmoval [1]_, [2]_. References ---------- .. [1] A. Carass, M.B. Wheeler, J. Cuzzocreo, P.-L. Bazin, S.S. Bassett, and J.L. Prince, 'A Joint Registration and Segmentation Approach to Skull Stripping', Fourth IEEE International Symposium on Biomedical Imaging (ISBI 2007), Arlington, VA, April 12-15, 2007. .. [2] A. Carass, J. Cuzzocreo, M.B. Wheeler, P.-L. Bazin, S.M. Resnick, and J.L. Prince, 'Simple paradigm for extra-cerebral tissue removal: Algorithm and analysis', NeuroImage 56(4):1982-1992, 2011. """ input_spec = MedicAlgorithmSPECTRE2010InputSpec output_spec = MedicAlgorithmSPECTRE2010OutputSpec _cmd = "java edu.jhu.ece.iacl.jist.cli.run edu.jhu.ece.iacl.plugins.segmentation.skull_strip.MedicAlgorithmSPECTRE2010 " _outputs_filenames = { "outd0": "outd0.nii", "outOriginal": "outOriginal.nii", "outMask": "outMask.nii", "outSplitHalves": "outSplitHalves.nii", "outMidsagittal": "outMidsagittal.nii", "outPrior": "outPrior.nii", "outFANTASM": "outFANTASM.nii", "outSegmentation": "outSegmentation.nii", "outStripped": "outStripped.nii", } _redirect_x = True class JistBrainPartialVolumeFilterInputSpec(CommandLineInputSpec): inInput = File(desc="Input Image", exists=True, argstr="--inInput %s") inPV = traits.Enum( "bright", "dark", "both", desc="Outputs the raw intensity values or a probability score for the partial volume regions.", argstr="--inPV %s", ) inoutput = traits.Enum( "probability", "intensity", desc="output", argstr="--inoutput %s" ) xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s") outPartial = traits.Either( traits.Bool, File(), hash_files=False, desc="Partial Volume Image", argstr="--outPartial %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 JistBrainPartialVolumeFilterOutputSpec(TraitedSpec): outPartial = File(desc="Partial Volume Image", exists=True) class JistBrainPartialVolumeFilter(SEMLikeCommandLine): """Partial Volume Filter. Filters an image for regions of partial voluming assuming a ridge-like model of intensity. """ input_spec = JistBrainPartialVolumeFilterInputSpec output_spec = JistBrainPartialVolumeFilterOutputSpec _cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.brain.JistBrainPartialVolumeFilter " _outputs_filenames = {"outPartial": "outPartial.nii"} _redirect_x = True class JistIntensityMp2rageMaskingInputSpec(CommandLineInputSpec): inSecond = File( desc="Second inversion (Inv2) Image", exists=True, argstr="--inSecond %s" ) inQuantitative = File( desc="Quantitative T1 Map (T1_Images) Image", exists=True, argstr="--inQuantitative %s", ) inT1weighted = File( desc="T1-weighted (UNI) Image", exists=True, argstr="--inT1weighted %s" ) inBackground = traits.Enum( "exponential", "half-normal", desc="Model distribution for background noise (default is half-normal, exponential is more stringent).", argstr="--inBackground %s", ) inSkip = traits.Enum("true", "false", desc="Skip zero values", argstr="--inSkip %s") inMasking = traits.Enum( "binary", "proba", desc="Whether to use a binary threshold or a weighted average based on the probability.", argstr="--inMasking %s", ) xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s") outSignal = traits.Either( traits.Bool, File(), hash_files=False, desc="Signal Proba Image", argstr="--outSignal_Proba %s", ) outSignal2 = traits.Either( traits.Bool, File(), hash_files=False, desc="Signal Mask Image", argstr="--outSignal_Mask %s", ) outMasked = traits.Either( traits.Bool, File(), hash_files=False, desc="Masked T1 Map Image", argstr="--outMasked_T1_Map %s", ) outMasked2 = traits.Either( traits.Bool, File(), hash_files=False, desc="Masked Iso Image", argstr="--outMasked_T1weighted %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 JistIntensityMp2rageMaskingOutputSpec(TraitedSpec): outSignal = File(desc="Signal Proba Image", exists=True) outSignal2 = File(desc="Signal Mask Image", exists=True) outMasked = File(desc="Masked T1 Map Image", exists=True) outMasked2 = File(desc="Masked Iso Image", exists=True) class JistIntensityMp2rageMasking(SEMLikeCommandLine): """Estimate a background signal mask for a MP2RAGE dataset.""" input_spec = JistIntensityMp2rageMaskingInputSpec output_spec = JistIntensityMp2rageMaskingOutputSpec _cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.intensity.JistIntensityMp2rageMasking " _outputs_filenames = { "outSignal2": "outSignal2.nii", "outSignal": "outSignal.nii", "outMasked2": "outMasked2.nii", "outMasked": "outMasked.nii", } _redirect_x = True class MedicAlgorithmThresholdToBinaryMaskInputSpec(CommandLineInputSpec): inLabel = InputMultiPath(File, desc="Input volumes", sep=";", argstr="--inLabel %s") inMinimum = traits.Float(desc="Minimum threshold value.", argstr="--inMinimum %f") inMaximum = traits.Float(desc="Maximum threshold value.", argstr="--inMaximum %f") inUse = traits.Enum( "true", "false", desc="Use the images max intensity as the max value of the range.", argstr="--inUse %s", ) xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s") outBinary = InputMultiPath( File, desc="Binary Mask", sep=";", argstr="--outBinary %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 MedicAlgorithmThresholdToBinaryMaskOutputSpec(TraitedSpec): pass class MedicAlgorithmThresholdToBinaryMask(SEMLikeCommandLine): """Threshold to Binary Mask. Given a volume and an intensity range create a binary mask for values within that range. """ input_spec = MedicAlgorithmThresholdToBinaryMaskInputSpec output_spec = MedicAlgorithmThresholdToBinaryMaskOutputSpec _cmd = "java edu.jhu.ece.iacl.jist.cli.run edu.jhu.ece.iacl.plugins.utilities.volume.MedicAlgorithmThresholdToBinaryMask " _outputs_filenames = {} _redirect_x = True