def test_BRAINSConstellationDetector_outputs():
output_map = dict(outputLandmarksInACPCAlignedSpace=dict(),
outputLandmarksInInputSpace=dict(),
outputMRML=dict(),
outputResampledVolume=dict(),
outputTransform=dict(),
outputUntransformedClippedVolume=dict(),
outputVerificationScript=dict(),
outputVolume=dict(),
resultsDir=dict(),
writeBranded2DImage=dict(),
)
outputs = BRAINSConstellationDetector.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_BRAINSConstellationDetector_outputs():
output_map = dict(
outputLandmarksInACPCAlignedSpace=dict(),
outputLandmarksInInputSpace=dict(),
outputMRML=dict(),
outputResampledVolume=dict(),
outputTransform=dict(),
outputUntransformedClippedVolume=dict(),
outputVerificationScript=dict(),
outputVolume=dict(),
resultsDir=dict(),
writeBranded2DImage=dict(),
)
outputs = BRAINSConstellationDetector.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def CreateLandmarkInitializeWorkflow(WFname, master_config, InterpolationMode, PostACPCAlignToAtlas, DoReverseInit, useEMSP=False, Debug=False):
CLUSTER_QUEUE=master_config['queue']
CLUSTER_QUEUE_LONG=master_config['long_q']
landmarkInitializeWF = pe.Workflow(name=WFname)
#############
inputsSpec = pe.Node(interface=IdentityInterface(fields=['inputVolume',
'atlasLandmarkFilename',
'atlasWeightFilename',
'LLSModel',
'inputTemplateModel',
'atlasVolume',
'EMSP']),
run_without_submitting=True,
name='inputspec')
#############
outputsSpec = pe.Node(interface=IdentityInterface(fields=['outputLandmarksInACPCAlignedSpace',
'outputResampledVolume', 'outputResampledCroppedVolume',
'outputLandmarksInInputSpace',
'writeBranded2DImage',
'outputTransform', 'outputMRML', 'atlasToSubjectTransform'
]),
run_without_submitting=True,
name='outputspec')
########################################################/
# Run ACPC Detect on first T1 Image - Base Image
########################################################
BCD = pe.Node(interface=BRAINSConstellationDetector(), name="BCD")
many_cpu_BCD_options_dictionary = {'qsub_args': modify_qsub_args(CLUSTER_QUEUE_LONG,4,2,4), 'overwrite': True}
BCD.plugin_args = many_cpu_BCD_options_dictionary
## Use program default BCD.inputs.inputTemplateModel = T1ACPCModelFile
# BCD.inputs.outputVolume = "BCD_OUT" + "_ACPC_InPlace.nii.gz" #$# T1AcpcImageList
BCD.inputs.outputTransform = "BCD" + "_Original2ACPC_transform.h5"
BCD.inputs.outputResampledVolume = "BCD" + "_ACPC.nii.gz"
BCD.inputs.outputLandmarksInInputSpace = "BCD" + "_Original.fcsv"
BCD.inputs.outputLandmarksInACPCAlignedSpace = "BCD" + "_ACPC_Landmarks.fcsv"
BCD.inputs.writeBranded2DImage = "BCD"+"_Branded2DQCimage.png"
# BCD.inputs.outputMRML = "BCD" + "_Scene.mrml"
BCD.inputs.interpolationMode = InterpolationMode
BCD.inputs.houghEyeDetectorMode = 1 # Look for dark eyes like on a T1 image, 0=Look for bright eyes like in a T2 image
BCD.inputs.acLowerBound = 80.0 # Chop the data set 80mm below the AC PC point.
# Entries below are of the form:
landmarkInitializeWF.connect(inputsSpec, 'inputVolume', BCD, 'inputVolume')
landmarkInitializeWF.connect(inputsSpec, 'atlasWeightFilename', BCD, 'atlasLandmarkWeights')
landmarkInitializeWF.connect(inputsSpec, 'atlasLandmarkFilename',BCD, 'atlasLandmarks')
landmarkInitializeWF.connect(inputsSpec, 'LLSModel', BCD, 'LLSModel')
landmarkInitializeWF.connect(inputsSpec, 'inputTemplateModel', BCD, 'inputTemplateModel')
# If EMSP, pre-selected landmarks are given, force to use.
if useEMSP:
print("*** Use pre-selected landmark file for Landmark Detection")
landmarkInitializeWF.connect(inputsSpec, 'EMSP', BCD, 'inputLandmarksEMSP')
# If the atlas volume is from this subject (i.e. after template building for the longitudinal phase) then set this to True
# Otherwise, it is probably best to let the ACPC alignment be fully defined by the landmark points themselves.
if PostACPCAlignToAtlas:
landmarkInitializeWF.connect(inputsSpec, 'atlasVolume', BCD, 'atlasVolume')
########################################################
# Run BLI atlas_to_subject
########################################################
BLI = pe.Node(interface=BRAINSLandmarkInitializer(), name="BLI")
BLI.inputs.outputTransformFilename = "landmarkInitializer_atlas_to_subject_transform.h5"
landmarkInitializeWF.connect(inputsSpec, 'atlasWeightFilename', BLI, 'inputWeightFilename')
landmarkInitializeWF.connect(inputsSpec, 'atlasLandmarkFilename', BLI, 'inputMovingLandmarkFilename')
landmarkInitializeWF.connect(BCD, 'outputLandmarksInACPCAlignedSpace', BLI, 'inputFixedLandmarkFilename')
## This is for debugging purposes, and it is not intended for general use.
if DoReverseInit == True:
########################################################
# Run BLI subject_to_atlas
########################################################
BLI2Atlas = pe.Node(interface=BRAINSLandmarkInitializer(), name="BLI2Atlas")
BLI2Atlas.inputs.outputTransformFilename = "landmarkInitializer_subject_to_atlas_transform.h5"
landmarkInitializeWF.connect(inputsSpec, 'atlasWeightFilename', BLI2Atlas, 'inputWeightFilename')
landmarkInitializeWF.connect(inputsSpec, 'atlasLandmarkFilename', BLI2Atlas, 'inputFixedLandmarkFilename')
landmarkInitializeWF.connect(BCD, 'outputLandmarksInInputSpace', BLI2Atlas, 'inputMovingLandmarkFilename')
Resample2Atlas = pe.Node(interface=BRAINSResample(), name="Resample2Atlas")
Resample2Atlas.inputs.interpolationMode = "Linear"
Resample2Atlas.inputs.outputVolume = "subject2atlas.nii.gz"
landmarkInitializeWF.connect(inputsSpec, 'inputVolume', Resample2Atlas, 'inputVolume')
landmarkInitializeWF.connect(BLI2Atlas, 'outputTransformFilename', Resample2Atlas, 'warpTransform')
if (DoReverseInit == True) and (Debug == True):
ResampleFromAtlas = pe.Node(interface=BRAINSResample(), name="ResampleFromAtlas")
ResampleFromAtlas.inputs.interpolationMode = "Linear"
ResampleFromAtlas.inputs.outputVolume = "atlas2subject.nii.gz"
landmarkInitializeWF.connect(inputsSpec, 'atlasVolume', ResampleFromAtlas, 'inputVolume')
landmarkInitializeWF.connect(BLI, 'outputTransformFilename', ResampleFromAtlas, 'warpTransform')
landmarkInitializeWF.connect(BCD, 'outputResampledVolume', ResampleFromAtlas, 'referenceVolume')
BROIAUTO = pe.Node(interface=BRAINSROIAuto(), name="BROIAuto_cropped")
many_cpu_BROIAUTO_options_dictionary = {'qsub_args': modify_qsub_args(CLUSTER_QUEUE_LONG,4,2,4), 'overwrite': True}
BROIAUTO.plugin_args = many_cpu_BROIAUTO_options_dictionary
BROIAUTO.inputs.outputVolume = "Cropped_BCD_ACPC_Aligned.nii.gz"
BROIAUTO.inputs.ROIAutoDilateSize = 10
BROIAUTO.inputs.cropOutput = True
landmarkInitializeWF.connect(BCD, 'outputResampledVolume', BROIAUTO, 'inputVolume')
landmarkInitializeWF.connect(BROIAUTO, 'outputVolume', outputsSpec, 'outputResampledCroppedVolume')
landmarkInitializeWF.connect(BCD, 'outputLandmarksInACPCAlignedSpace', outputsSpec, 'outputLandmarksInACPCAlignedSpace')
landmarkInitializeWF.connect(BCD, 'outputResampledVolume', outputsSpec, 'outputResampledVolume')
landmarkInitializeWF.connect(BCD, 'outputLandmarksInInputSpace', outputsSpec, 'outputLandmarksInInputSpace')
landmarkInitializeWF.connect(BCD, 'outputTransform', outputsSpec, 'outputTransform')
landmarkInitializeWF.connect(BCD, 'outputMRML', outputsSpec, 'outputMRML')
landmarkInitializeWF.connect(BCD, 'writeBranded2DImage', outputsSpec, 'writeBranded2DImage')
landmarkInitializeWF.connect(BLI, 'outputTransformFilename', outputsSpec, 'atlasToSubjectTransform')
return landmarkInitializeWF
def test_BRAINSConstellationDetector_inputs():
input_map = dict(BackgroundFillValue=dict(argstr='--BackgroundFillValue %s',
),
LLSModel=dict(argstr='--LLSModel %s',
),
acLowerBound=dict(argstr='--acLowerBound %f',
),
args=dict(argstr='%s',
),
atlasLandmarkWeights=dict(argstr='--atlasLandmarkWeights %s',
),
atlasLandmarks=dict(argstr='--atlasLandmarks %s',
),
atlasVolume=dict(argstr='--atlasVolume %s',
),
cutOutHeadInOutputVolume=dict(argstr='--cutOutHeadInOutputVolume ',
),
debug=dict(argstr='--debug ',
),
environ=dict(nohash=True,
usedefault=True,
),
forceACPoint=dict(argstr='--forceACPoint %s',
sep=',',
),
forceHoughEyeDetectorReportFailure=dict(argstr='--forceHoughEyeDetectorReportFailure ',
),
forcePCPoint=dict(argstr='--forcePCPoint %s',
sep=',',
),
forceRPPoint=dict(argstr='--forceRPPoint %s',
sep=',',
),
forceVN4Point=dict(argstr='--forceVN4Point %s',
sep=',',
),
houghEyeDetectorMode=dict(argstr='--houghEyeDetectorMode %d',
),
ignore_exception=dict(nohash=True,
usedefault=True,
),
inputLandmarksEMSP=dict(argstr='--inputLandmarksEMSP %s',
),
inputTemplateModel=dict(argstr='--inputTemplateModel %s',
),
inputVolume=dict(argstr='--inputVolume %s',
),
interpolationMode=dict(argstr='--interpolationMode %s',
),
mspQualityLevel=dict(argstr='--mspQualityLevel %d',
),
numberOfThreads=dict(argstr='--numberOfThreads %d',
),
otsuPercentileThreshold=dict(argstr='--otsuPercentileThreshold %f',
),
outputLandmarksInACPCAlignedSpace=dict(argstr='--outputLandmarksInACPCAlignedSpace %s',
hash_files=False,
),
outputLandmarksInInputSpace=dict(argstr='--outputLandmarksInInputSpace %s',
hash_files=False,
),
outputMRML=dict(argstr='--outputMRML %s',
hash_files=False,
),
outputResampledVolume=dict(argstr='--outputResampledVolume %s',
hash_files=False,
),
outputTransform=dict(argstr='--outputTransform %s',
hash_files=False,
),
outputUntransformedClippedVolume=dict(argstr='--outputUntransformedClippedVolume %s',
hash_files=False,
),
outputVerificationScript=dict(argstr='--outputVerificationScript %s',
hash_files=False,
),
outputVolume=dict(argstr='--outputVolume %s',
hash_files=False,
),
rVN4=dict(argstr='--rVN4 %f',
),
rac=dict(argstr='--rac %f',
),
rescaleIntensities=dict(argstr='--rescaleIntensities ',
),
rescaleIntensitiesOutputRange=dict(argstr='--rescaleIntensitiesOutputRange %s',
sep=',',
),
resultsDir=dict(argstr='--resultsDir %s',
hash_files=False,
),
rmpj=dict(argstr='--rmpj %f',
),
rpc=dict(argstr='--rpc %f',
),
terminal_output=dict(nohash=True,
),
trimRescaledIntensities=dict(argstr='--trimRescaledIntensities %f',
),
verbose=dict(argstr='--verbose ',
),
writeBranded2DImage=dict(argstr='--writeBranded2DImage %s',
hash_files=False,
),
writedebuggingImagesLevel=dict(argstr='--writedebuggingImagesLevel %d',
),
)
inputs = BRAINSConstellationDetector.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_BRAINSConstellationDetector_inputs():
input_map = dict(
BackgroundFillValue=dict(argstr='--BackgroundFillValue %s', ),
LLSModel=dict(argstr='--LLSModel %s', ),
acLowerBound=dict(argstr='--acLowerBound %f', ),
args=dict(argstr='%s', ),
atlasLandmarkWeights=dict(argstr='--atlasLandmarkWeights %s', ),
atlasLandmarks=dict(argstr='--atlasLandmarks %s', ),
atlasVolume=dict(argstr='--atlasVolume %s', ),
cutOutHeadInOutputVolume=dict(argstr='--cutOutHeadInOutputVolume ', ),
debug=dict(argstr='--debug ', ),
environ=dict(
nohash=True,
usedefault=True,
),
forceACPoint=dict(
argstr='--forceACPoint %s',
sep=',',
),
forceHoughEyeDetectorReportFailure=dict(
argstr='--forceHoughEyeDetectorReportFailure ', ),
forcePCPoint=dict(
argstr='--forcePCPoint %s',
sep=',',
),
forceRPPoint=dict(
argstr='--forceRPPoint %s',
sep=',',
),
forceVN4Point=dict(
argstr='--forceVN4Point %s',
sep=',',
),
houghEyeDetectorMode=dict(argstr='--houghEyeDetectorMode %d', ),
ignore_exception=dict(
nohash=True,
usedefault=True,
),
inputLandmarksEMSP=dict(argstr='--inputLandmarksEMSP %s', ),
inputTemplateModel=dict(argstr='--inputTemplateModel %s', ),
inputVolume=dict(argstr='--inputVolume %s', ),
interpolationMode=dict(argstr='--interpolationMode %s', ),
mspQualityLevel=dict(argstr='--mspQualityLevel %d', ),
numberOfThreads=dict(argstr='--numberOfThreads %d', ),
otsuPercentileThreshold=dict(argstr='--otsuPercentileThreshold %f', ),
outputLandmarksInACPCAlignedSpace=dict(
argstr='--outputLandmarksInACPCAlignedSpace %s',
hash_files=False,
),
outputLandmarksInInputSpace=dict(
argstr='--outputLandmarksInInputSpace %s',
hash_files=False,
),
outputMRML=dict(
argstr='--outputMRML %s',
hash_files=False,
),
outputResampledVolume=dict(
argstr='--outputResampledVolume %s',
hash_files=False,
),
outputTransform=dict(
argstr='--outputTransform %s',
hash_files=False,
),
outputUntransformedClippedVolume=dict(
argstr='--outputUntransformedClippedVolume %s',
hash_files=False,
),
outputVerificationScript=dict(
argstr='--outputVerificationScript %s',
hash_files=False,
),
outputVolume=dict(
argstr='--outputVolume %s',
hash_files=False,
),
rVN4=dict(argstr='--rVN4 %f', ),
rac=dict(argstr='--rac %f', ),
rescaleIntensities=dict(argstr='--rescaleIntensities ', ),
rescaleIntensitiesOutputRange=dict(
argstr='--rescaleIntensitiesOutputRange %s',
sep=',',
),
resultsDir=dict(
argstr='--resultsDir %s',
hash_files=False,
),
rmpj=dict(argstr='--rmpj %f', ),
rpc=dict(argstr='--rpc %f', ),
terminal_output=dict(nohash=True, ),
trimRescaledIntensities=dict(argstr='--trimRescaledIntensities %f', ),
verbose=dict(argstr='--verbose ', ),
writeBranded2DImage=dict(
argstr='--writeBranded2DImage %s',
hash_files=False,
),
writedebuggingImagesLevel=dict(
argstr='--writedebuggingImagesLevel %d', ),
)
inputs = BRAINSConstellationDetector.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def CreateLandmarkInitializeWorkflow(WFname,
InterpolationMode,
DoReverseInit=False,
debug=False):
landmarkInitializeWF = pe.Workflow(name=WFname)
#############
inputsSpec = pe.Node(interface=IdentityInterface(fields=[
'inputVolume', 'atlasLandmarkFilename', 'atlasWeightFilename',
'LLSModel', 'inputTemplateModel', 'atlasVolume'
]),
run_without_submitting=True,
name='inputspec')
#############
outputsSpec = pe.Node(interface=IdentityInterface(fields=[
'outputLandmarksInACPCAlignedSpace', 'outputResampledVolume',
'outputResampledCroppedVolume', 'outputLandmarksInInputSpace',
'writeBranded2DImage', 'outputTransform', 'outputMRML',
'atlasToSubjectTransform'
]),
run_without_submitting=True,
name='outputspec')
########################################################/
# Run ACPC Detect on first T1 Image - Base Image
########################################################
BCD = pe.Node(interface=BRAINSConstellationDetector(), name="BCD")
## Use program default BCD.inputs.inputTemplateModel = T1ACPCModelFile
# BCD.inputs.outputVolume = "BCD_OUT" + "_ACPC_InPlace.nii.gz" #$# T1AcpcImageList
BCD.inputs.outputTransform = "BCD" + "_Original2ACPC_transform.h5"
BCD.inputs.outputResampledVolume = "BCD" + "_ACPC.nii.gz"
BCD.inputs.outputLandmarksInInputSpace = "BCD" + "_Original.fcsv"
BCD.inputs.outputLandmarksInACPCAlignedSpace = "BCD" + "_ACPC_Landmarks.fcsv"
BCD.inputs.writeBranded2DImage = "BCD" + "_Branded2DQCimage.png"
# BCD.inputs.outputMRML = "BCD" + "_Scene.mrml"
BCD.inputs.interpolationMode = InterpolationMode
BCD.inputs.houghEyeDetectorMode = 1 # Look for dark eyes like on a T1 image, 0=Look for bright eyes like in a T2 image
BCD.inputs.acLowerBound = 80.0 # Chop the data set 80mm below the AC PC point.
# Entries below are of the form:
landmarkInitializeWF.connect(inputsSpec, 'inputVolume', BCD, 'inputVolume')
landmarkInitializeWF.connect(inputsSpec, 'atlasWeightFilename', BCD,
'atlasLandmarkWeights')
landmarkInitializeWF.connect(inputsSpec, 'atlasLandmarkFilename', BCD,
'atlasLandmarks')
landmarkInitializeWF.connect(inputsSpec, 'LLSModel', BCD, 'LLSModel')
landmarkInitializeWF.connect(inputsSpec, 'inputTemplateModel', BCD,
'inputTemplateModel')
landmarkInitializeWF.connect(inputsSpec, 'atlasVolume', BCD, 'atlasVolume')
########################################################
# Run BLI atlas_to_subject
########################################################
BLI = pe.Node(interface=BRAINSLandmarkInitializer(), name="BLI")
BLI.inputs.outputTransformFilename = "landmarkInitializer_atlas_to_subject_transform.h5"
landmarkInitializeWF.connect(inputsSpec, 'atlasWeightFilename', BLI,
'inputWeightFilename')
landmarkInitializeWF.connect(inputsSpec, 'atlasLandmarkFilename', BLI,
'inputMovingLandmarkFilename')
landmarkInitializeWF.connect(BCD, 'outputLandmarksInACPCAlignedSpace', BLI,
'inputFixedLandmarkFilename')
## This is for debugging purposes, and it is not intended for general use.
if DoReverseInit == True:
########################################################
# Run BLI subject_to_atlas
########################################################
BLI2Atlas = pe.Node(interface=BRAINSLandmarkInitializer(),
name="BLI2Atlas")
BLI2Atlas.inputs.outputTransformFilename = "landmarkInitializer_subject_to_atlas_transform.h5"
landmarkInitializeWF.connect(inputsSpec, 'atlasWeightFilename',
BLI2Atlas, 'inputWeightFilename')
landmarkInitializeWF.connect(inputsSpec, 'atlasLandmarkFilename',
BLI2Atlas, 'inputFixedLandmarkFilename')
landmarkInitializeWF.connect(BCD, 'outputLandmarksInInputSpace',
BLI2Atlas, 'inputMovingLandmarkFilename')
Resample2Atlas = pe.Node(interface=BRAINSResample(),
name="Resample2Atlas")
Resample2Atlas.inputs.interpolationMode = "Linear"
Resample2Atlas.inputs.outputVolume = "subject2atlas.nii.gz"
landmarkInitializeWF.connect(inputsSpec, 'inputVolume', Resample2Atlas,
'inputVolume')
landmarkInitializeWF.connect(BLI2Atlas, 'outputTransformFilename',
Resample2Atlas, 'warpTransform')
landmarkInitializeWF.connect(inputsSpec, 'atlasVolume', Resample2Atlas,
'referenceVolume')
if (DoReverseInit == True) and (debug == True):
ResampleFromAtlas = pe.Node(interface=BRAINSResample(),
name="ResampleFromAtlas")
ResampleFromAtlas.inputs.interpolationMode = "Linear"
ResampleFromAtlas.inputs.outputVolume = "atlas2subject.nii.gz"
landmarkInitializeWF.connect(inputsSpec, 'atlasVolume',
ResampleFromAtlas, 'inputVolume')
landmarkInitializeWF.connect(BLI, 'outputTransformFilename',
ResampleFromAtlas, 'warpTransform')
landmarkInitializeWF.connect(BCD, 'outputResampledVolume',
ResampleFromAtlas, 'referenceVolume')
BROIAUTO = pe.Node(interface=BRAINSROIAuto(), name="BROIAuto_cropped")
BROIAUTO.inputs.outputVolume = "Cropped_BCD_ACPC_Aligned.nii.gz"
BROIAUTO.inputs.ROIAutoDilateSize = 10
BROIAUTO.inputs.cropOutput = True
landmarkInitializeWF.connect(BCD, 'outputResampledVolume', BROIAUTO,
'inputVolume')
landmarkInitializeWF.connect(BROIAUTO, 'outputVolume', outputsSpec,
'outputResampledCroppedVolume')
landmarkInitializeWF.connect(BCD, 'outputLandmarksInACPCAlignedSpace',
outputsSpec,
'outputLandmarksInACPCAlignedSpace')
landmarkInitializeWF.connect(BCD, 'outputResampledVolume', outputsSpec,
'outputResampledVolume')
landmarkInitializeWF.connect(BCD, 'outputLandmarksInInputSpace',
outputsSpec, 'outputLandmarksInInputSpace')
landmarkInitializeWF.connect(BCD, 'outputTransform', outputsSpec,
'outputTransform')
landmarkInitializeWF.connect(BCD, 'outputMRML', outputsSpec, 'outputMRML')
landmarkInitializeWF.connect(BCD, 'writeBranded2DImage', outputsSpec,
'writeBranded2DImage')
landmarkInitializeWF.connect(BLI, 'outputTransformFilename', outputsSpec,
'atlasToSubjectTransform')
return landmarkInitializeWF
