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Added more variable descriptions.
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‎nipype/interfaces/cat12/preprocess.py‎

Lines changed: 37 additions & 27 deletions
Original file line numberDiff line numberDiff line change
@@ -18,7 +18,6 @@ class CAT12SegmentInputSpec(SPMCommandInputSpec):
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copyfile=False)
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n_jobs = traits.Int(1, usedefault=True, mandatory=True, field="nproc", desc="Number of threads")
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use_prior = Str(field="useprior", usedefault=True)
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_help_affine_reg = 'Affine Regularization. The procedure is a local optimisation, so it needs reasonable initial ' \
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'starting estimates. Images should be placed in approximate alignment using the Display ' \
@@ -129,64 +128,75 @@ class CAT12SegmentInputSpec(SPMCommandInputSpec):
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'further analysis! For distinction, these files contain "preview" in their filename and they' \
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' are not available as batch dependencies objects. '
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surface_and_thickness_estimation = traits.Int(1, field="surface", desc=_help_surf, usedefault=True)
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surface_measures = traits.Int(1, field="output.surf_measures", usedefault=True)
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surface_measures = traits.Int(1, field="output.surf_measures", usedefault=True, desc="Extract surface measures")
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# Templates
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neuromorphometrics = traits.Bool(True, field="output.ROImenu.atlases.neuromorphometrics",
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usedefault=True)
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lpba40 = traits.Bool(True, field="output.ROImenu.atlases.lpba40", usedefault=True)
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cobra = traits.Bool(True, field="output.ROImenu.atlases.hammers", usedefault=True)
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hammers = traits.Bool(True, field="output.ROImenu.atlases.cobra", usedefault=True)
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usedefault=True, desc="Extract brain measures for Neuromorphometrics template")
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lpba40 = traits.Bool(True, field="output.ROImenu.atlases.lpba40", usedefault=True,
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desc="Extract brain measures for LPBA40 template")
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cobra = traits.Bool(True, field="output.ROImenu.atlases.hammers", usedefault=True,
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desc="Extract brain measures for COBRA template")
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hammers = traits.Bool(True, field="output.ROImenu.atlases.cobra", usedefault=True,
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desc="Extract brain measures for Hammers template")
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own_atlas = InputMultiPath(ImageFileSPM(exists=True), field="output.ROImenu.atlases.ownatlas",
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desc="Own Atlas", mandatory=False, copyfile=False)
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desc="Extract brain measures for a given template", mandatory=False, copyfile=False)
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_dartel_help = 'This option is to export data into a form that can be used with DARTEL. The SPM default is to ' \
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'only apply rigid body transformation. However, a more appropriate option is to apply affine ' \
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'transformation, because the additional scaling of the images requires less deformations to ' \
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'non-linearly register brains to the template.'
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# Grey matter
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_gm_desc = 'Options to save grey matter images.'
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gm_output_native = traits.Bool(False, field="output.GM.native", usedefault=True, desc=_gm_desc)
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gm_output_modulated = traits.Bool(True, field="output.GM.mod", usedefault=True, desc=_gm_desc)
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gm_output_dartel = traits.Bool(False, field="output.GM.dartel", usedefault=True, desc=_gm_desc)
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gm_output_native = traits.Bool(False, field="output.GM.native", usedefault=True,
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desc='Save modulated grey matter images.')
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gm_output_modulated = traits.Bool(True, field="output.GM.mod", usedefault=True,
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desc='Save native grey matter images.')
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gm_output_dartel = traits.Bool(False, field="output.GM.dartel", usedefault=True,
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desc='Save dartel grey matter images.')
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# White matter
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_wm_desc = 'Options to save white matter images.'
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wm_output_native = traits.Bool(False, field="output.WM.native", usedefault=True, desc=_wm_desc)
157-
wm_output_modulated = traits.Bool(True, field="output.WM.mod", usedefault=True, desc=_wm_desc)
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wm_output_dartel = traits.Bool(False, field="output.WM.dartel", usedefault=True, desc=_wm_desc)
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wm_output_native = traits.Bool(False, field="output.WM.native", usedefault=True,
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desc='Save dartel white matter images.')
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wm_output_modulated = traits.Bool(True, field="output.WM.mod", usedefault=True,
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desc='Save dartel white matter images.')
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wm_output_dartel = traits.Bool(False, field="output.WM.dartel", usedefault=True,
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desc='Save dartel white matter images.')
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# CSF matter
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_csf_desc = 'Options to save CSF images.'
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csf_output_native = traits.Bool(False, field="output.CSF.native", usedefault=True, desc=_csf_desc)
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csf_output_modulated = traits.Bool(True, field="output.CSF.mod", usedefault=True, desc=_csf_desc)
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csf_output_dartel = traits.Bool(False, field="output.CSF.dartel", usedefault=True, desc=_csf_desc)
169+
csf_output_native = traits.Bool(False, field="output.CSF.native", usedefault=True,
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desc='Save dartel CSF images.')
171+
csf_output_modulated = traits.Bool(True, field="output.CSF.mod", usedefault=True,
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desc='Save dartel CSF images.')
173+
csf_output_dartel = traits.Bool(False, field="output.CSF.dartel", usedefault=True,
174+
desc='Save dartel CSF images.')
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# Labels
167-
_help_label_desc = 'This is the option to save a labeled version of your segmentations for fast visual ' \
177+
_help_label_desc = 'This is the option to save a labeled version of your segmentations in the %s space for fast visual ' \
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'comparision. Labels are saved as Partial Volume Estimation (PVE) values with different mix ' \
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'classes for GM-WM (2.5) and GM-CSF (1.5). BG=0, CSF=1, GM=2, WM=3, WMH=4 (if WMHC=3), ' \
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'SL=1.5 (if SLC)'
171-
label_native = traits.Bool(False, field="output.label.native", usedefault=True, desc=_help_label_desc)
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label_warped = traits.Bool(True, field="output.label.warped", usedefault=True, desc=_help_label_desc)
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label_dartel = traits.Bool(False, field="output.label.dartel", usedefault=True, desc=_help_label_desc)
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output_labelnative = traits.Bool(False, field="output.labelnative", usedefault=True, desc=_help_label_desc)
181+
label_native = traits.Bool(False, field="output.label.native", usedefault=True, desc=_help_label_desc % "native")
182+
label_warped = traits.Bool(True, field="output.label.warped", usedefault=True, desc=_help_label_desc % "warped")
183+
label_dartel = traits.Bool(False, field="output.label.dartel", usedefault=True, desc=_help_label_desc % "dartel")
184+
output_labelnative = traits.Bool(False, field="output.labelnative", usedefault=True, desc=_help_label_desc % "native")
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# Bias
177-
save_bias_corrected = traits.Bool(True, field="output.bias.warped", usedefault=True)
187+
save_bias_corrected = traits.Bool(True, field="output.bias.warped", usedefault=True, desc="Save bias corrected image")
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# las
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_las_desc = 'This is the option to save a bias, noise, and local intensity corrected version of the original T1' \
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' image. MR images are usually corrupted by a smooth, spatially varying artifact that modulates the' \
191+
' image in the %s space. MR images are usually corrupted by a smooth, spatially varying artifact that modulates the' \
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' intensity of the image (bias). These artifacts, although not usually a problem for visual ' \
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'inspection, can impede automated processing of the images. The bias corrected version should have ' \
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'more uniform intensities within the different types of tissues and can be saved in native space ' \
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'and/or normalised. Noise is corrected by an adaptive non-local mean (NLM) filter (Manjon 2008, ' \
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'Medical Image Analysis 12).'
187-
las_native = traits.Bool(False, field="output.las.native", usedefault=True, desc=_las_desc)
188-
las_warped = traits.Bool(True, field="output.las.warped", usedefault=True, desc=_las_desc)
189-
las_dartel = traits.Bool(False, field="output.las.dartel", usedefault=True, desc=_las_desc)
197+
las_native = traits.Bool(False, field="output.las.native", usedefault=True, desc=_las_desc % "native")
198+
las_warped = traits.Bool(True, field="output.las.warped", usedefault=True, desc=_las_desc % "warped")
199+
las_dartel = traits.Bool(False, field="output.las.dartel", usedefault=True, desc=_las_desc % "dartel")
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# Jacobian Warped
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_help_jacobian = 'This is the option to save the Jacobian determinant, which expresses local volume changes. This' \

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