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Added LesionTOADS
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‎nipype/interfaces/mipav/__init__.py

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from developer import MedicAlgorithmSPECTRE2010, JistIntensityMp2rageMasking
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from developer import MedicAlgorithmLesionToads, JistCortexSurfaceMeshInflation, MedicAlgorithmImageCalculator, JistBrainMp2rageDuraEstimation, MedicAlgorithmSPECTRE2010, JistBrainPartialVolumeFilter, JistIntensityMp2rageMasking

‎nipype/interfaces/mipav/developer.py

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import os
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class MedicAlgorithmLesionToadsInputSpec(CommandLineInputSpec):
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maxMemoryUsage = traits.Int(desc="Maximum Memory Allowed (in MegaBytes). Increase or decrease this depending on java virtual machine heap size requirements.", argstr="--maxMemoryUsage %d")
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inT1_MPRAGE = File(desc="T1_MPRAGE Image", exists=True, argstr="--inT1_MPRAGE %s")
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inT1_SPGR = File(desc="T1_SPGR Image", exists=True, argstr="--inT1_SPGR %s")
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inFLAIR = File(desc="FLAIR Image", exists=True, argstr="--inFLAIR %s")
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inAtlas = traits.Enum("With Lesion", "No Lesion", desc="Atlas to Use", argstr="--inAtlas %s")
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inOutput = traits.Enum("hard segmentation", "hard segmentation+memberships", "cruise inputs", "dura removal inputs", desc="Output images", argstr="--inOutput %s")
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inOutput2 = traits.Enum("true", "false", desc="Output the hard classification using maximum membership (not neceesarily topologically correct)", argstr="--inOutput2 %s")
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inCorrect = traits.Enum("true", "false", desc="Correct MR field inhomogeneity.", argstr="--inCorrect %s")
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inOutput3 = traits.Enum("true", "false", desc="Output the estimated inhomogeneity field", argstr="--inOutput3 %s")
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inAtlas2 = File(desc="Atlas File - With Lesions", exists=True, argstr="--inAtlas2 %s")
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inAtlas3 = File(desc="Atlas File - No Lesion - T1 and FLAIR", exists=True, argstr="--inAtlas3 %s")
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inAtlas4 = File(desc="Atlas File - No Lesion - T1 Only", exists=True, argstr="--inAtlas4 %s")
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inMaximum = traits.Int(desc="Maximum distance from the interventricular WM boundary to downweight the lesion membership to avoid false postives", argstr="--inMaximum %d")
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inMaximum2 = traits.Int(desc="Maximum Ventircle Distance", argstr="--inMaximum2 %d")
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inMaximum3 = traits.Int(desc="Maximum InterVentricular Distance", argstr="--inMaximum3 %d")
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inInclude = traits.Enum("true", "false", desc="Include lesion in WM class in hard classification", argstr="--inInclude %s")
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inAtlas5 = traits.Float(desc="Controls the effect of the statistical atlas on the segmentation", argstr="--inAtlas5 %f")
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inSmooting = traits.Float(desc="Controls the effect of neighberhood voxels on the membership", argstr="--inSmooting %f")
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inMaximum4 = traits.Float(desc="Maximum amount of relative change in the energy function considered as the convergence criteria", argstr="--inMaximum4 %f")
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inMaximum5 = traits.Int(desc="Maximum iterations", argstr="--inMaximum5 %d")
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inAtlas6 = traits.Enum("rigid", "multi_fully_affine", desc="Atlas alignment", argstr="--inAtlas6 %s")
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inConnectivity = traits.Enum("(26,6)", "(6,26)", "(6,18)", "(18,6)", desc="Connectivity (foreground,background)", argstr="--inConnectivity %s")
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xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
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outHard = traits.Either(traits.Bool, File(), hash_files=False, desc="Hard segmentation", argstr="--outHard %s")
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outHard2 = traits.Either(traits.Bool, File(), hash_files=False, desc="Hard segmentationfrom memberships", argstr="--outHard2 %s")
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outInhomogeneity = traits.Either(traits.Bool, File(), hash_files=False, desc="Inhomogeneity Field", argstr="--outInhomogeneity %s")
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outMembership = traits.Either(traits.Bool, File(), hash_files=False, desc="Membership Functions", argstr="--outMembership %s")
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outLesion = traits.Either(traits.Bool, File(), hash_files=False, desc="Lesion Segmentation", argstr="--outLesion %s")
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outSulcal = traits.Either(traits.Bool, File(), hash_files=False, desc="Sulcal CSF Membership", argstr="--outSulcal %s")
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outCortical = traits.Either(traits.Bool, File(), hash_files=False, desc="Cortical GM Membership", argstr="--outCortical %s")
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outFilled = traits.Either(traits.Bool, File(), hash_files=False, desc="Filled WM Membership", argstr="--outFilled %s")
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outWM = traits.Either(traits.Bool, File(), hash_files=False, desc="WM Mask", argstr="--outWM %s")
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outExecution = traits.Str(desc="Execution Time", argstr="--outExecution %s")
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class MedicAlgorithmLesionToadsOutputSpec(TraitedSpec):
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outHard = File(desc="Hard segmentation", exists=True)
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outHard2 = File(desc="Hard segmentationfrom memberships", exists=True)
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outInhomogeneity = File(desc="Inhomogeneity Field", exists=True)
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outMembership = File(desc="Membership Functions", exists=True)
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outLesion = File(desc="Lesion Segmentation", exists=True)
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outSulcal = File(desc="Sulcal CSF Membership", exists=True)
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outCortical = File(desc="Cortical GM Membership", exists=True)
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outFilled = File(desc="Filled WM Membership", exists=True)
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outWM = File(desc="WM Mask", exists=True)
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class MedicAlgorithmLesionToads(SEMLikeCommandLine):
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"""title: Lesion TOADS
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category: Developer Tools
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description: 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.
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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.
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version: 1.7.R
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contributor: Navid Shiee ([email protected]) http://iacl.ece.jhu.edu/~nshiee/
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"""
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input_spec = MedicAlgorithmLesionToadsInputSpec
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output_spec = MedicAlgorithmLesionToadsOutputSpec
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_cmd = "java edu.jhu.ece.iacl.jist.cli.run edu.jhu.ece.iacl.plugins.classification.MedicAlgorithmLesionToads "
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_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'}
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_redirect_x = True
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class JistCortexSurfaceMeshInflationInputSpec(CommandLineInputSpec):
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maxMemoryUsage = traits.Int(desc="Maximum Memory Allowed (in MegaBytes). Increase or decrease this depending on java virtual machine heap size requirements.", argstr="--maxMemoryUsage %d")
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inLevelset = File(desc="Levelset Image", exists=True, argstr="--inLevelset %s")
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inSOR = traits.Float(desc="SOR Parameter", argstr="--inSOR %f")
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inMean = traits.Float(desc="Mean Curvature Threshold", argstr="--inMean %f")
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inStep = traits.Int(desc="Step Size", argstr="--inStep %d")
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inMax = traits.Int(desc="Max Iterations", argstr="--inMax %d")
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inLorentzian = traits.Enum("true", "false", desc="Lorentzian Norm", argstr="--inLorentzian %s")
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inTopology = traits.Enum("26/6", "6/26", "18/6", "6/18", "6/6", "wcs", "wco", "no", desc="Topology", argstr="--inTopology %s")
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xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
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outOriginal = traits.Either(traits.Bool, File(), hash_files=False, desc="Original Surface", argstr="--outOriginal %s")
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outInflated = traits.Either(traits.Bool, File(), hash_files=False, desc="Inflated Surface", argstr="--outInflated %s")
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outExecution = traits.Str(desc="Execution Time", argstr="--outExecution %s")
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class JistCortexSurfaceMeshInflationOutputSpec(TraitedSpec):
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outOriginal = File(desc="Original Surface", exists=True)
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outInflated = File(desc="Inflated Surface", exists=True)
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class JistCortexSurfaceMeshInflation(SEMLikeCommandLine):
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"""title: Surface Mesh Inflation
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category: Developer Tools
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description: Inflates a cortical surface mesh.
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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.
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version: 3.0.RC
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contributor: Duygu Tosun
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"""
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input_spec = JistCortexSurfaceMeshInflationInputSpec
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output_spec = JistCortexSurfaceMeshInflationOutputSpec
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_cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.cortex.JistCortexSurfaceMeshInflation "
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_outputs_filenames = {'outOriginal':'outOriginal','outInflated':'outInflated'}
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_redirect_x = True
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class MedicAlgorithmImageCalculatorInputSpec(CommandLineInputSpec):
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maxMemoryUsage = traits.Int(desc="Maximum Memory Allowed (in MegaBytes). Increase or decrease this depending on java virtual machine heap size requirements.", argstr="--maxMemoryUsage %d")
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inVolume = File(desc="Volume 1", exists=True, argstr="--inVolume %s")
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inVolume2 = File(desc="Volume 2", exists=True, argstr="--inVolume2 %s")
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inOperation = traits.Enum("Add", "Subtract", "Multiply", "Divide", "Min", "Max", desc="Operation", argstr="--inOperation %s")
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xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
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outResult = traits.Either(traits.Bool, File(), hash_files=False, desc="Result Volume", argstr="--outResult %s")
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outExecution = traits.Str(desc="Execution Time", argstr="--outExecution %s")
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class MedicAlgorithmImageCalculatorOutputSpec(TraitedSpec):
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outResult = File(desc="Result Volume", exists=True)
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class MedicAlgorithmImageCalculator(SEMLikeCommandLine):
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"""title: Image Calculator
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category: Developer Tools
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description: Perform simple image calculator operations on two images. The operations include 'Add', 'Subtract', 'Multiply', and 'Divide'
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version: 1.10.RC
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documentation-url: http://www.iacl.ece.jhu.edu/
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"""
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input_spec = MedicAlgorithmImageCalculatorInputSpec
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output_spec = MedicAlgorithmImageCalculatorOutputSpec
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_cmd = "java edu.jhu.ece.iacl.jist.cli.run edu.jhu.ece.iacl.plugins.utilities.math.MedicAlgorithmImageCalculator "
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_outputs_filenames = {'outResult':'outResult.nii'}
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_redirect_x = True
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class JistBrainMp2rageDuraEstimationInputSpec(CommandLineInputSpec):
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maxMemoryUsage = traits.Int(desc="Maximum Memory Allowed (in MegaBytes). Increase or decrease this depending on java virtual machine heap size requirements.", argstr="--maxMemoryUsage %d")
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inSecond = File(desc="Second inversion (Inv2) Image", exists=True, argstr="--inSecond %s")
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inSkull = File(desc="Skull Stripping Mask", exists=True, argstr="--inSkull %s")
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inDistance = traits.Float(desc="Distance to background (mm)", argstr="--inDistance %f")
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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")
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xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
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outDura = traits.Either(traits.Bool, File(), hash_files=False, desc="Dura Image", argstr="--outDura %s")
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outExecution = traits.Str(desc="Execution Time", argstr="--outExecution %s")
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class JistBrainMp2rageDuraEstimationOutputSpec(TraitedSpec):
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outDura = File(desc="Dura Image", exists=True)
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class JistBrainMp2rageDuraEstimation(SEMLikeCommandLine):
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"""title: MP2RAGE Dura Estimation
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category: Developer Tools
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description: Filters a MP2RAGE brain image to obtain a probability map of dura matter.
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version: 3.0.RC
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"""
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input_spec = JistBrainMp2rageDuraEstimationInputSpec
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output_spec = JistBrainMp2rageDuraEstimationOutputSpec
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_cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.brain.JistBrainMp2rageDuraEstimation "
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_outputs_filenames = {'outDura':'outDura.nii'}
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_redirect_x = True
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class MedicAlgorithmSPECTRE2010InputSpec(CommandLineInputSpec):
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maxMemoryUsage = traits.Int(desc="Maximum Memory Allowed (in MegaBytes). Increase or decrease this depending on java virtual machine heap size requirements.", argstr="--maxMemoryUsage %d")
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inInput = File(desc="Input volume to be skullstripped.", exists=True, argstr="--inInput %s")
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_redirect_x = True
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class JistBrainPartialVolumeFilterInputSpec(CommandLineInputSpec):
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maxMemoryUsage = traits.Int(desc="Maximum Memory Allowed (in MegaBytes). Increase or decrease this depending on java virtual machine heap size requirements.", argstr="--maxMemoryUsage %d")
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inInput = File(desc="Input Image", exists=True, argstr="--inInput %s")
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inPV = traits.Enum("bright", "dark", "both", desc="Outputs the raw intensity values or a probability score for the partial volume regions.", argstr="--inPV %s")
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inoutput = traits.Enum("probability", "intensity", desc="output", argstr="--inoutput %s")
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xPrefExt = traits.Enum("nrrd", desc="Output File Type", argstr="--xPrefExt %s")
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outPartial = traits.Either(traits.Bool, File(), hash_files=False, desc="Partial Volume Image", argstr="--outPartial %s")
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outExecution = traits.Str(desc="Execution Time", argstr="--outExecution %s")
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class JistBrainPartialVolumeFilterOutputSpec(TraitedSpec):
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outPartial = File(desc="Partial Volume Image", exists=True)
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class JistBrainPartialVolumeFilter(SEMLikeCommandLine):
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"""title: Partial Volume Filter
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category: Developer Tools
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description: Filters an image for regions of partial voluming assuming a ridge-like model of intensity.
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version: 2.0.RC
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"""
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input_spec = JistBrainPartialVolumeFilterInputSpec
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output_spec = JistBrainPartialVolumeFilterOutputSpec
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_cmd = "java edu.jhu.ece.iacl.jist.cli.run de.mpg.cbs.jist.brain.JistBrainPartialVolumeFilter "
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_outputs_filenames = {'outPartial':'outPartial.nii'}
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_redirect_x = True
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class JistIntensityMp2rageMaskingInputSpec(CommandLineInputSpec):
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maxMemoryUsage = traits.Int(desc="Maximum Memory Allowed (in MegaBytes). Increase or decrease this depending on java virtual machine heap size requirements.", argstr="--maxMemoryUsage %d")
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inSecond = File(desc="Second inversion (Inv2) Image", exists=True, argstr="--inSecond %s")

‎nipype/interfaces/mipav/generate_classes.py

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'de.mpg.cbs.jist.modules.JistModuleRelabelSegmentation',
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'de.mpg.cbs.jist.modules.JistModuleAggregativeSliceClustering',
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#'de.mpg.cbs.jist.modules.JistModuleMgdmMultiSegmentation',
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'de.mpg.cbs.jist.modules.JistModuleCorticalProfileFeatures',
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#'de.mpg.cbs.jist.modules.JistModuleCorticalProfileFeatures',
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'de.mpg.cbs.jist.modules.JistModuleCorticalProfileMeshSampling',
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'de.mpg.cbs.jist.modules.JistModuleIntensityBounds',
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'de.mpg.cbs.jist.modules.JistModuleCorticalMeshMapping',
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#'de.mpg.cbs.jist.modules.JistModuleCorticalInflation',
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'de.mpg.cbs.jist.modules.JistModuleExtractBrainRegion',
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'de.mpg.cbs.jist.modules.JistModuleAtlasBasedSimulator',
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'de.mpg.cbs.jist.modules.JistModuleProbabilityToLevelset',
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#'de.mpg.cbs.jist.modules.JistModuleProbabilityToLevelset',
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#'de.mpg.cbs.jist.modules.JistModuleROISimilarity',
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'de.mpg.cbs.jist.modules.JistModuleROIMembership',
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'de.mpg.cbs.jist.modules.JistModuleRenameImage',
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#'de.mpg.cbs.jist.modules.JistModuleCopyData', XML not well formed
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#'de.mpg.cbs.jist.tools.JistToolsIntensityNormalization',
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#'de.mpg.cbs.jist.tools.JistToolsExtractBrainRegion',
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'edu.jhu.ece.iacl.plugins.utilities.volume.MedicAlgorithmThresholdToBinaryMask',# XML not well formed
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#'edu.jhu.ece.iacl.plugins.utilities.volume.MedicAlgorithmThresholdToBinaryMask',# XML not well formed
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#'de.mpg.cbs.jist.cortex.JistCortexFullCRUISE',
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'de.mpg.cbs.jist.cortex.JistCortexSurfaceMeshInflation']
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modules_from_julia = ['de.mpg.cbs.jist.intensity.JistIntensityMp2rageMasking',
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'edu.jhu.ece.iacl.plugins.segmentation.skull_strip.MedicAlgorithmSPECTRE2010']
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modules_from_leaonie = ['edu.jhu.ece.iacl.plugins.classification.MedicAlgorithmLesionToads']
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#modules_list = list(set(modules_list).union(set(modules_from_chris)))
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modules_list = modules_from_julia
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modules_list = list(set(modules_from_chris).union(set(modules_from_leaonie)).union(set(modules_from_julia)))
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## SlicerExecutionModel compliant tools that are usually statically built, and don't need the Slicer3 --launcher
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generate_all_classes(modules_list=modules_list,launcher=["java edu.jhu.ece.iacl.jist.cli.run" ], redirect_x = True)

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