def view(image):
have_imglyb = False
try:
import imglyb
have_imglyb = True
except ImportError:
pass
have_vtk = False
try:
import vtk
have_vtk = True
except ImportError:
pass
viewer = Viewer()
if is_arraylike(image):
arr = np.asarray(image)
image_from_array = itk.GetImageViewFromArray(arr)
viewer.image = image_from_array
elif have_vtk and isinstance(image, vtk.vtkImageData):
from vtk.util import numpy_support as vtk_numpy_support
array = vtk_numpy_support.vtk_to_numpy(image.GetPointData().GetScalars())
array.shape = tuple(image.GetDimensions())[::-1]
image_from_array = itk.GetImageViewFromArray(array)
image_from_array.SetSpacing(image.GetSpacing())
image_from_array.SetOrigin(image.GetOrigin())
viewer.image = image_from_array
elif have_imglyb and isinstance(image,
imglyb.util.ReferenceGuardingRandomAccessibleInterval):
image_array = imglyb.to_numpy(image)
image_from_array = itk.GetImageViewFromArray(image_array)
else:
# an itk.Image
viewer.image = itk.output(image)
return viewer
def to_itk_image(other_image_datatype):
if is_arraylike(other_image_datatype):
array = np.asarray(other_image_datatype)
case_use_view = array.flags['OWNDATA']
if have_dask and isinstance(other_image_datatype,
dask.array.core.Array):
case_use_view = False
if case_use_view:
image_from_array = itk.GetImageViewFromArray(array)
else:
image_from_array = itk.GetImageFromArray(array)
return image_from_array
elif have_vtk and isinstance(other_image_datatype, vtk.vtkImageData):
from vtk.util import numpy_support as vtk_numpy_support
array = vtk_numpy_support.vtk_to_numpy(
other_image_datatype.GetPointData().GetScalars())
array.shape = tuple(other_image_datatype.GetDimensions())[::-1]
image_from_array = itk.GetImageViewFromArray(array)
image_from_array.SetSpacing(other_image_datatype.GetSpacing())
image_from_array.SetOrigin(other_image_datatype.GetOrigin())
return image_from_array
elif have_imglyb and isinstance(
other_image_datatype,
imglyb.util.ReferenceGuardingRandomAccessibleInterval):
array = imglyb.to_numpy(other_image_datatype)
image_from_array = itk.GetImageViewFromArray(array)
return image_from_array
return None
def view(image):
viewer = Viewer()
if isinstance(image, np.ndarray):
image_from_array = itk.GetImageViewFromArray(image)
viewer.image = image_from_array
elif HAVE_VTK and isinstance(image, vtk.vtkImageData):
from vtk.util import numpy_support as vtk_numpy_support
array = vtk_numpy_support.vtk_to_numpy(
image.GetPointData().GetScalars())
array.shape = tuple(image.GetDimensions())[::-1]
image_from_array = itk.GetImageViewFromArray(array)
image_from_array.SetSpacing(image.GetSpacing())
image_from_array.SetOrigin(image.GetOrigin())
viewer.image = image_from_array
else:
viewer.image = image
return viewer
def itk_ws(self, NEED_WL, res_type):
Dimension = 3
#dist_itk = create_itk_img(self.dist.shape, Dimension, itk.US)
#markers_itk = create_itk_img(self.dist.shape, Dimension, itk.US)
dist_itk = itk.GetImageViewFromArray(self.dist)
markers_itk = itk.GetImageViewFromArray(self.markers)
#np_view = itk.GetArrayViewFromImage(dist_itk)
#np_mar = itk.GetArrayViewFromImage(markers_itk)
#np_view[:] = self.dist
#np_mar[:] = self.markers
labelImage, res = decorator(Image_3D._itk_ws,
res_type)(self, NEED_WL, dist_itk,
markers_itk)
if labelImage is not None:
self.labels[:] = itk.GetArrayViewFromImage(labelImage)
return res
def dataset_to_itk_image(dataset):
import itk
itk_image = itk.GetImageViewFromArray(dataset.active_scalars)
if dataset.spacing is not None:
itk_image.SetSpacing(dataset.spacing)
return itk_image
def validate(self, obj, value):
if is_arraylike(value):
array = np.asarray(value)
self._source_object = array
if array.flags['OWNDATA']:
image_from_array = itk.GetImageViewFromArray(array)
else:
image_from_array = itk.GetImageFromArray(array)
return image_from_array
elif have_vtk and isinstance(value, vtk.vtkImageData):
from vtk.util import numpy_support as vtk_numpy_support
array = vtk_numpy_support.vtk_to_numpy(
value.GetPointData().GetScalars())
array.shape = tuple(value.GetDimensions())[::-1]
self._source_object = array
image_from_array = itk.GetImageViewFromArray(array)
image_from_array.SetSpacing(value.GetSpacing())
image_from_array.SetOrigin(value.GetOrigin())
return image_from_array
elif have_imglyb and isinstance(
value, imglyb.util.ReferenceGuardingRandomAccessibleInterval):
array = imglyb.to_numpy(value)
self._source_object = array
image_from_array = itk.GetImageViewFromArray(array)
return image_from_array
try:
# an itk.Image or a filter that produces an Image
# return itk.output(value)
# Working around traitlets / ipywidgets update mechanism to
# force an update. While the result of __eq__ can indicate it is
# the same object, the actual contents may have changed, as
# indicated by image.GetMTime()
value = itk.output(value)
self._source_object = value
grafted = value.__New_orig__()
grafted.Graft(value)
return grafted
except:
self.error(obj, value)
import numpy as np image = itk.imread(filename) arr = itk.array_from_image(image) arr.fill(1) assert np.any(arr != itk.array_from_image(image)) arr = itk.array_from_image(image) arr.fill(1) assert np.any(arr != itk.array_from_image(image)) view = itk.GetArrayViewFromImage(image) view.fill(1) assert np.all(view == itk.array_from_image(image)) image = itk.image_from_array(arr) image.FillBuffer(2) assert np.any(arr != itk.array_from_image(image)) image = itk.GetImageViewFromArray(arr) image.FillBuffer(2) assert np.all(arr == itk.array_from_image(image)) image = itk.image_from_array(arr, is_vector=True) assert image.GetImageDimension() == 2 image = itk.GetImageViewFromArray(arr, is_vector=True) assert image.GetImageDimension() == 2 arr = np.array([[1, 2, 3], [4, 5, 6]]).astype(np.uint8) assert arr.shape[0] == 2 assert arr.shape[1] == 3 assert arr[1, 1] == 5 image = itk.image_from_array(arr) arrKeepAxes = itk.array_from_image(image, keep_axes=True) assert arrKeepAxes.shape[0] == 3 assert arrKeepAxes.shape[1] == 2 assert arrKeepAxes[1, 1] == 4
def connected_components(dataset, background_value=0, progress_callback=None):
try:
import numpy as np
import itk
except Exception as exc:
print("Could not import necessary module(s)")
print(exc)
if np.issubdtype(dataset.active_scalars.dtype, np.floating):
raise Exception(
"Connected Components works only on images with integral types.")
# Add a try/except around the ITK portion. ITK exceptions are
# passed up to the Python layer, so we can at least report what
# went wrong with the script, e.g,, unsupported image type.
try:
# Get the ITK image. The input is assumed to have an integral type.
# Take care of casting to an unsigned short image so we can store up
# to 65,535 connected components (the number of connected components
# is limited to the maximum representable number in the voxel type
# of the input image in the ConnectedComponentsFilter).
array = dataset.active_scalars.astype(np.uint16)
itk_image = itk.GetImageViewFromArray(array)
itk_image.SetSpacing(dataset.spacing)
itk_image_type = type(itk_image)
# ConnectedComponentImageFilter
connected_filter = itk.ConnectedComponentImageFilter[
itk_image_type, itk_image_type].New()
connected_filter.SetBackgroundValue(background_value)
connected_filter.SetInput(itk_image)
if progress_callback is not None:
def connected_progress_func():
progress = connected_filter.GetProgress()
abort = progress_callback(progress * 0.5)
connected_filter.SetAbortGenerateData(abort)
connected_observer = itk.PyCommand.New()
connected_observer.SetCommandCallable(connected_progress_func)
connected_filter.AddObserver(itk.ProgressEvent(),
connected_observer)
# Relabel filter. This will compress the label numbers to a
# continugous range between 1 and n where n is the number of
# labels. It will also sort the components from largest to
# smallest, where the largest component has label 1, the
# second largest has label 2, and so on...
relabel_filter = itk.RelabelComponentImageFilter[itk_image_type,
itk_image_type].New()
relabel_filter.SetInput(connected_filter.GetOutput())
relabel_filter.SortByObjectSizeOn()
if progress_callback is not None:
def relabel_progress_func():
progress = relabel_filter.GetProgress()
abort = progress_callback(progress * 0.5 + 0.5)
relabel_filter.SetAbortGenerateData(abort)
relabel_observer = itk.PyCommand.New()
relabel_observer.SetCommandCallable(relabel_progress_func)
relabel_filter.AddObserver(itk.ProgressEvent(), relabel_observer)
try:
relabel_filter.Update()
except RuntimeError:
return
itk_image_data = relabel_filter.GetOutput()
label_buffer = itk.PyBuffer[itk_image_type].GetArrayFromImage(
itk_image_data)
# Flip the labels so that the largest component has the highest label
# value, e.g., the labeling ordering by size goes from [1, 2, ... N] to
# [N, N-1, N-2, ..., 1]. Note that zero is the background value, so we
# do not want to change it.
import numpy as np
minimum = 1 # Minimum label is always 1, background is 0
maximum = np.max(label_buffer)
# Try more memory-efficient approach
gt_zero = label_buffer > 0
label_buffer[gt_zero] = minimum - label_buffer[gt_zero] + maximum
# Transpose the data to Fortran indexing
dataset.active_scalars = label_buffer.transpose([2, 1, 0])
except Exception as exc:
print("Problem encountered while running ConnectedComponents")
raise exc
def main(main_args):
print("<b>>>> Running " + os.path.basename(sys.argv[0]) + "</b>\n")
sys.stdout.flush()
### Inputs
T1 = main_args.t1
T2 = main_args.t2
T2_exists = True
if (T2 == ""):
T2_exists = False
T1_split = os.path.splitext(os.path.basename(T1))
if (T1_split[1] == 'gz'):
T1_base = os.path.splitext(T1_split[0])
else:
T1_base = T1_split[0]
if (T2_exists):
T2_split = os.path.splitext(os.path.basename(T2))
if (T2_split[1] == 'gz'):
T2_base = os.path.splitext(T2_split[0])
else:
T2_base = T2_split[0]
### Executables
python = main_args.python3
ImageMath = main_args.ImageMath
ImageStat = main_args.ImageStat
ABC = main_args.ABC
### Masks
CSFLabel = int(main_args.CSFLabel)
### Output path
OUT_PATH = main_args.output
### Scripts
scripts_prefix = os.path.join(OUT_PATH, "PythonScripts")
if (main_args.registration == "true"):
rigid_align_script = os.path.join(scripts_prefix,
"rigid_align_script.py")
print_main_info("Running " + rigid_align_script)
OUT_RR = os.path.join(OUT_PATH, 'RigidRegistration')
call([python, rigid_align_script, '--output', OUT_RR])
T1_REGISTERED = os.path.join(OUT_RR, "".join([T1_base, "_stx.nrrd"]))
if (T2_exists):
T2_REGISTERED = os.path.join(OUT_RR,
"".join([T2_base, "_stx.nrrd"]))
print_main_info("Finished running " + rigid_align_script)
else:
T1_REGISTERED = T1
if (T2_exists):
T2_REGISTERED = T2
OUT_SS = os.path.join(OUT_PATH, 'SkullStripping')
if not os.path.exists(OUT_SS):
os.makedirs(OUT_SS)
if (main_args.skullStripping == "true"):
make_mask_script = os.path.join(scripts_prefix, "make_mask_script.py")
print_main_info("Running " + make_mask_script)
BRAIN_MASK = os.path.join(OUT_SS,
"".join([T1_base, "_FinalBrainMask.nrrd"]))
if not (os.path.isfile(BRAIN_MASK)):
if (T2_exists):
call([
python, make_mask_script, '--t1', T1_REGISTERED, '--t2',
T2_REGISTERED, '--at_dir', '--at_list', '--output', OUT_SS
])
else:
call([
python, make_mask_script, '--t1', T1_REGISTERED, '--t2',
'', '--at_dir', '--at_list', '--output', OUT_SS
])
else:
print('Brainmask already exists')
print_main_info("Finished running " + make_mask_script)
else:
BRAIN_MASK = main_args.brainMask
print_main_info("Stripping the skull")
T1_STRIPPED = os.path.join(OUT_SS, "".join([T1_base, "_stripped.nrrd"]))
args = [
ImageMath, T1_REGISTERED, '-outfile', T1_STRIPPED, '-mask', BRAIN_MASK
]
call_and_print(args)
if (T2_exists):
T2_STRIPPED = os.path.join(OUT_SS, "".join([T2_base,
"_stripped.nrrd"]))
args = [
ImageMath, T2_REGISTERED, '-outfile', T2_STRIPPED, '-mask',
BRAIN_MASK
]
call_and_print(args)
if (main_args.segmentation == "true"):
tissue_seg_script = os.path.join(scripts_prefix,
"tissue_seg_script.py")
print_main_info("Running " + tissue_seg_script)
OUT_TS = os.path.join(OUT_PATH, 'TissueSegAtlas')
OUT_ABC = os.path.join(OUT_PATH, 'ABC_Segmentation')
Segmentation = os.path.join(
OUT_ABC, "".join([T1_base, "_stripped_labels_EMS.nrrd"]))
print(Segmentation)
if not (os.path.isfile(Segmentation)):
print(Segmentation + ' doesnt exist')
if (T2_exists):
call([
python, tissue_seg_script, '--t1', T1_STRIPPED, '--t2',
T2_STRIPPED, '--at_dir', '--output', OUT_TS
])
else:
call([
python, tissue_seg_script, '--t1', T1_STRIPPED, '--t2', '',
'--at_dir', '--output', OUT_TS
])
else:
print('Segmentation already exists')
print_main_info("Finished running tissue_seg_script.py")
else:
Segmentation = main_args.tissueSeg
if (main_args.removeVentricles == "true"):
vent_mask_script = os.path.join(scripts_prefix, "vent_mask_script.py")
print_main_info("Running " + vent_mask_script)
OUT_VR = os.path.join(OUT_PATH, 'VentricleMasking')
call([
python, vent_mask_script, '--t1', T1_STRIPPED,
'--subjectTissueSeg', Segmentation, '--output', OUT_VR
])
Segmentation = os.path.join(
OUT_VR, "".join([T1_base, "_stripped_EMS_withoutVent.nrrd"]))
print_main_info("Finished running " + vent_mask_script)
BRAIN_MASK_base = os.path.splitext(os.path.basename(BRAIN_MASK))[0]
Segmentation_base = os.path.splitext(os.path.basename(Segmentation))[0]
######### Stripping the skull from segmentation ######
MID_TEMP00 = os.path.join(OUT_PATH, "".join([T1_base, "_MID00.nrrd"]))
args = [
ImageMath, Segmentation, '-outfile', MID_TEMP00, '-mask', BRAIN_MASK
]
call_and_print(args)
######### Cutting below AC-PC line #######
### Coronal mask creation
print_main_info("Cutting below AC-PC line")
if (main_args.cerebellumMask == ""):
ACPC_unit = main_args.ACPCunit
if (ACPC_unit == "index"):
ACPC_val = int(main_args.ACPCval)
else:
ACPC_mm = float(main_args.ACPCval)
im = itk.imread(T1_REGISTERED)
index_coord = im.TransformPhysicalPointToContinuousIndex(
[ACPC_mm, 0, 0])
ACPC_val = round(index_coord[0])
Coronal_Mask = "coronal_mask_" + str(ACPC_val) + ".nrrd"
if not (os.path.isfile(Coronal_Mask)):
im = itk.imread(T1_REGISTERED)
np_copy = itk.GetArrayFromImage(im)
if ((ACPC_val >= np_copy.shape[0]) | (ACPC_val <= 0)):
eprint("ACPC index out of range (" + str(ACPC_val) +
"), using default coronal mask (slice 70)")
sys.stderr.flush()
ACPC_val = 70
print_main_info('Creating coronal mask')
np_copy[ACPC_val - 1:np_copy.shape[0] - 1, :, :] = 1
np_copy[0:ACPC_val - 1, :, :] = 0
itk_np_copy = itk.GetImageViewFromArray(np_copy)
itk_np_copy.SetOrigin(im.GetOrigin())
itk_np_copy.SetSpacing(im.GetSpacing())
itk_np_copy.SetDirection(im.GetDirection())
itk.imwrite(itk_np_copy, Coronal_Mask)
print_main_info('Coronal mask created')
else:
print_main_info('Loading ' + Coronal_Mask)
else:
Coronal_Mask = main_args.cerebellumMask
### Mask multiplication
MID_TEMP01 = os.path.join(OUT_PATH,
"".join([Segmentation_base, "_MID01.nrrd"]))
MID_TEMP02 = os.path.join(OUT_PATH,
"".join([Segmentation_base, "_MID02.nrrd"]))
MID_TEMP03 = os.path.join(OUT_PATH,
"".join([Segmentation_base, "_MID03.nrrd"]))
args = [
ImageMath, MID_TEMP00, '-outfile', MID_TEMP01, '-mask', Coronal_Mask
]
call_and_print(args)
args = [
ImageMath, MID_TEMP01, '-outfile', MID_TEMP02, '-extractLabel', '3'
]
call_and_print(args)
args = [ImageMath, MID_TEMP02, '-outfile', MID_TEMP03, '-conComp', '1']
call_and_print(args)
Erosion_Mask = BRAIN_MASK_base + '_Erosion.nrrd'
Erosion_Mask00 = BRAIN_MASK_base + '_Erosion00.nrrd'
Erosion_Mask01 = BRAIN_MASK_base + '_Erosion01.nrrd'
Erosion_Mask01_inv = BRAIN_MASK_base + '_Erosion01_inv.nrrd'
args = [ImageMath, BRAIN_MASK, '-erode', '1,1', '-outfile', Erosion_Mask00]
call_and_print(args)
args = [
ImageMath, Erosion_Mask00, '-erode', '1,1', '-outfile', Erosion_Mask01
]
call_and_print(args)
for i in range(0, 14):
args = [
ImageMath, Erosion_Mask01, '-erode', '1,1', '-outfile',
Erosion_Mask01
]
call_and_print(args)
args = [
ImageMath, Erosion_Mask01, '-threshold', '0,0', '-outfile',
Erosion_Mask01_inv
]
call_and_print(args)
FINAL_CSF_SEG = Segmentation_base + "_FINAL_Partial_CSF.nrrd"
args = [
ImageMath, MID_TEMP03, '-mask', Erosion_Mask01_inv, '-outfile',
FINAL_CSF_SEG
]
call_and_print(args)
args = [
ImageMath, FINAL_CSF_SEG, '-mask', Coronal_Mask, '-outfile',
FINAL_CSF_SEG
]
call_and_print(args)
args = [
ImageMath, FINAL_CSF_SEG, '-mask', MID_TEMP02, '-outfile',
FINAL_CSF_SEG
]
call_and_print(args)
## add script erase quad....
Erosion = FINAL_CSF_SEG[:-5] + "_Ero.nrrd"
Erosion_MASKING = Erosion[:-5] + "_Masking.nrrd"
args = [ImageMath, FINAL_CSF_SEG, '-erode', '1,1', '-outfile', Erosion]
call_and_print(args)
args = [
ImageMath, Erosion_Mask01, '-erode', '1,1', '-outfile', Erosion_Mask
]
call_and_print(args)
for i in range(0, 5):
args = [
ImageMath, Erosion_Mask, '-erode', '1,1', '-outfile', Erosion_Mask
]
call_and_print(args)
args = [
ImageMath, Erosion, '-mask', Erosion_Mask, '-outfile', Erosion_MASKING
]
call_and_print(args)
Erosion_MASKING_conComp = Erosion_MASKING[:-5] + "_conComp.nrrd"
args = [
ImageMath, Erosion_MASKING, '-conComp', '1', '-outfile',
Erosion_MASKING_conComp
]
call_and_print(args)
Dilation_comComp = Erosion_MASKING_conComp[:-5] + "_dil.nrrd"
args = [
ImageMath, Erosion_MASKING_conComp, '-dilate', '1,1', '-outfile',
Dilation_comComp
]
call_and_print(args)
for k in range(0, 13):
args = [
ImageMath, Dilation_comComp, '-dilate', '1,1', '-outfile',
Dilation_comComp
]
call_and_print(args)
FINAL_RESULT = FINAL_CSF_SEG[:-5] + "_QCistern.nrrd"
args = [
ImageMath, Dilation_comComp, '-threshold', '0,0', '-outfile',
Dilation_comComp
]
call_and_print(args)
args = [
ImageMath, FINAL_CSF_SEG, '-mask', Dilation_comComp, '-outfile',
FINAL_RESULT
]
call_and_print(args)
if (main_args.computeCSFDensity == "true"):
LH_INNER_SURF = main_args.LHInnerSurf
RH_INNER_SURF = main_args.RHInnerSurf
OUT_CCD = os.path.join(OUT_PATH, 'CSF_Density')
args = [
'computecsfdensity', '-l', LH_INNER_SURF, '-r', RH_INNER_SURF,
'-s', Segmentation, '-c', FINAL_OUTERCSF, '-o', OUT_CCD, '-p',
Segmentation_base
]
call_and_print(args)
print_main_info("Auto_EACSF finished")
sys.exit(0)
def MotionCorrect(fixedImageFile,movingImageFile):
PixelType = itk.ctype('float')
fixedImage = itk.GetImageViewFromArray(fixedImageFile)
movingImage = itk.GetImageViewFromArray(movingImageFile)
# fixedImage = itk.imread(fixedImageFile, PixelType)
# movingImage = itk.imread(movingImageFile, PixelType)
Dimension = fixedImage.GetImageDimension()
FixedImageType = itk.Image[PixelType, Dimension]
MovingImageType = itk.Image[PixelType, Dimension]
TransformType = itk.TranslationTransform[itk.D, Dimension]
initialTransform = TransformType.New()
optimizer = itk.RegularStepGradientDescentOptimizerv4.New(
LearningRate=4,
MinimumStepLength=0.001,
RelaxationFactor=0.5,
NumberOfIterations=200)
metric = itk.MeanSquaresImageToImageMetricv4[
FixedImageType, MovingImageType].New()
registration = itk.ImageRegistrationMethodv4.New(FixedImage=fixedImage,
MovingImage=movingImage,
Metric=metric,
Optimizer=optimizer,
InitialTransform=initialTransform)
movingInitialTransform = TransformType.New()
initialParameters = movingInitialTransform.GetParameters()
initialParameters[0] = 0
initialParameters[1] = 0
movingInitialTransform.SetParameters(initialParameters)
registration.SetMovingInitialTransform(movingInitialTransform)
identityTransform = TransformType.New()
identityTransform.SetIdentity()
registration.SetFixedInitialTransform(identityTransform)
registration.SetNumberOfLevels(1)
registration.SetSmoothingSigmasPerLevel([0])
registration.SetShrinkFactorsPerLevel([1])
registration.Update()
# transform = registration.GetTransform()
# finalParameters = transform.GetParameters()
# translationAlongX = finalParameters.GetElement(0)
# translationAlongY = finalParameters.GetElement(1)
#
# numberOfIterations = optimizer.GetCurrentIteration()
#
# bestValue = optimizer.GetValue()
# print("Result = ")
# print(" Translation X = " + str(translationAlongX))
# print(" Translation Y = " + str(translationAlongY))
# print(" Iterations = " + str(numberOfIterations))
# print(" Metric value = " + str(bestValue))
CompositeTransformType = itk.CompositeTransform[itk.D, Dimension]
outputCompositeTransform = CompositeTransformType.New()
outputCompositeTransform.AddTransform(movingInitialTransform)
outputCompositeTransform.AddTransform(registration.GetModifiableTransform())
resampler = itk.ResampleImageFilter.New(Input=movingImage,
Transform=outputCompositeTransform,
UseReferenceImage=True,
ReferenceImage=fixedImage)
resampler.SetDefaultPixelValue(100)
OutputImageType = itk.Image[PixelType, Dimension]
caster = itk.CastImageFilter[FixedImageType,
OutputImageType].New(Input=resampler)
outputImageFile = itk.GetArrayFromImage(caster)
# writer = itk.ImageFileWriter.New(Input=caster, FileName=outputImageFile)
# writer.SetFileName(outputImageFile)
# writer.Update()
return outputImageFile
# BridgeNumPy
try:
# Images
import numpy
image = itk.imread(fileName)
arr = itk.GetArrayFromImage(image)
arr.fill(1)
assert numpy.any(arr != itk.GetArrayFromImage(image))
view = itk.GetArrayViewFromImage(image)
view.fill(1)
assert numpy.all(view == itk.GetArrayFromImage(image))
image = itk.GetImageFromArray(arr)
image.FillBuffer(2)
assert numpy.any(arr != itk.GetArrayFromImage(image))
image = itk.GetImageViewFromArray(arr)
image.FillBuffer(2)
assert numpy.all(arr == itk.GetArrayFromImage(image))
# VNL Vectors
v1 = itk.vnl_vector.D(2)
v1.fill(1)
v_np = itk.GetArrayFromVnlVector(v1)
assert v1.get(0) == v_np[0]
v_np[0] = 0
assert v1.get(0) != v_np[0]
view = itk.GetArrayViewFromVnlVector(v1)
assert v1.get(0) == view[0]
view[0] = 0
assert v1.get(0) == view[0]
# VNL Matrices
m1 = itk.vnl_matrix.D(2, 2)
def reorient(img, new_orientation=ITK_COORDINATE_ORIENTATION_LPI, new_dir = standard_dir):
# this function is implemented using ITK since SimpleITK has not implemented the filter "OrientImageFilter" yet
# the ITK orientation system is from this blog post: https://itk.org/pipermail/insight-users/2017-May/054606.html
# comparison ITK - simpleITK filters: https://itk.org/SimpleITKDoxygen/html/Filter_Coverage.html
# see also: https://github.com/fedorov/lidc-idri-conversion/blob/master/seg/seg_converter.py
# change image name
img_sitk = img
# get characteristics of simpleITK image
size_in_sitk = img_sitk.GetSize()
spacing_in_sitk = img_sitk.GetSpacing()
origin_in_sitk = img_sitk.GetOrigin()
direction_in_sitk = img_sitk.GetDirection()
# allocate ITK image (type and size)
Dimension = 3
PixelType = itk.F
ImageTypeIn = itk.Image[PixelType, Dimension]
img_itk = ImageTypeIn.New()
sizeIn_itk = itk.Size[Dimension]()
for i in range (0,Dimension):
sizeIn_itk[i] = size_in_sitk[i]
region = itk.ImageRegion[Dimension]()
region.SetSize(sizeIn_itk)
img_itk.SetRegions(region)
img_itk.Allocate()
# pass image from simpleITK to numpy
img_py = sitk.GetArrayFromImage(img_sitk)
# pass image from numpy to ITK
img_itk = itk.GetImageViewFromArray(img_py)
# pass characteristics from simpleITK image to ITK image (except size, assigned in allocation)
spacing_in_itk = itk.Vector[itk.F, Dimension]()
for i in range (0,Dimension):
spacing_in_itk[i] = spacing_in_sitk[i]
img_itk.SetSpacing(spacing_in_itk)
origin_in_itk = itk.Point[itk.F, Dimension]()
for i in range (0,Dimension):
origin_in_itk[i] = origin_in_sitk[i]
img_itk.SetOrigin(origin_in_itk)
# old way of assigning direction (until ITK 4.13)
# direction_in_itk = itk.Matrix[itk.F,3,3]()
# direction_in_itk = img_itk.GetDirection().GetVnlMatrix().set(0,0,direction_in_sitk[0]) # r,c,value
# direction_in_itk = img_itk.GetDirection().GetVnlMatrix().set(0,1,direction_in_sitk[1])
# direction_in_itk = img_itk.GetDirection().GetVnlMatrix().set(0,2,direction_in_sitk[2])
# direction_in_itk = img_itk.GetDirection().GetVnlMatrix().set(1,0,direction_in_sitk[3]) # r,c,value
# direction_in_itk = img_itk.GetDirection().GetVnlMatrix().set(1,1,direction_in_sitk[4])
# direction_in_itk = img_itk.GetDirection().GetVnlMatrix().set(1,2,direction_in_sitk[5])
# direction_in_itk = img_itk.GetDirection().GetVnlMatrix().set(2,0,direction_in_sitk[6]) # r,c,value
# direction_in_itk = img_itk.GetDirection().GetVnlMatrix().set(2,1,direction_in_sitk[7])
# direction_in_itk = img_itk.GetDirection().GetVnlMatrix().set(2,2,direction_in_sitk[8])
direction_in_itk = np.eye(3)
direction_in_itk[0][0] = direction_in_sitk[0]
direction_in_itk[0][1] = direction_in_sitk[1]
direction_in_itk[0][2] = direction_in_sitk[2]
direction_in_itk[1][0] = direction_in_sitk[3]
direction_in_itk[1][1] = direction_in_sitk[4]
direction_in_itk[1][2] = direction_in_sitk[5]
direction_in_itk[2][0] = direction_in_sitk[6]
direction_in_itk[2][1] = direction_in_sitk[7]
direction_in_itk[2][2] = direction_in_sitk[8]
img_itk.SetDirection(itk.matrix_from_array(direction_in_itk))
# make sure image is float for the orientation filter (GetImageViewFromArray sets it to unsigned char)
ImageTypeIn_afterPy = type(img_itk)
ImageTypeOut = itk.Image[itk.F, 3]
CastFilterType = itk.CastImageFilter[ImageTypeIn_afterPy, ImageTypeOut]
castFilter = CastFilterType.New()
castFilter.SetInput(img_itk)
castFilter.Update()
img_itk = castFilter.GetOutput()
# change orientation to RAI
OrientType = itk.OrientImageFilter[ImageTypeOut,ImageTypeOut]
filter = OrientType.New()
filter.UseImageDirectionOn()
filter.SetDesiredCoordinateOrientation(new_orientation)
filter.SetInput(img_itk)
filter.Update()
img_itk = filter.GetOutput()
# get characteristics of ITK image
spacing_out_itk = img_itk.GetSpacing()
origin_out_itk = img_itk.GetOrigin()
# pass image from itk to numpy
img_py = itk.GetArrayViewFromImage(img_itk)
# pass image from numpy to simpleitk
img = sitk.GetImageFromArray(img_py)
# pass characteristics from ITK image to simpleITK image (except size, implicitely passed)
spacing = []
for i in range (0, Dimension):
spacing.append(spacing_out_itk[i])
img.SetSpacing(spacing)
origin = []
for i in range (0, Dimension):
origin.append(origin_out_itk[i])
img.SetOrigin(origin)
img.SetDirection(new_dir)
return img
print("Processing Pixel...")
z = 0
y = 0
x = 0
for z in range(OutputArray.shape[0]):
for y in range(OutputArray.shape[1]):
for x in range(OutputArray.shape[2]):
if (OutputArray[z][y][x] != 0):
if (MaskArray[z][y][x] == 0):
OutputArray[z][y][x] = 0
else:
OutputArray[z][y][x] = 1
final = itk.GetImageViewFromArray(OutputArray)
final.SetOrigin(reader4mm.GetOutput().GetOrigin())
final.SetSpacing(reader4mm.GetOutput().GetSpacing())
final.SetDirection(reader4mm.GetOutput().GetDirection())
WriterType = itk.ImageFileWriter[LabeledImageType]
writer = WriterType.New()
writer.SetFileName(path + "/mask.mha")
writer.SetInput(final)
try:
writer.Update()
except Exception as error:
print("EXIT FAILURE")
print(error)
sys.exit(1)
# Read input image itk_image = itk.imread(input_filename) #run filters on itkImage #view only of itkImage, data is not copied np_view = itk.GetArrayViewFromImage(itk_image) #copy of itkImage, data is copied np_copy = itk.GetArrayFromImage(itk_image) #do numpy stuff #convert back to itk, view only, data is not copied itk_np_view = itk.GetImageViewFromArray(np_copy) #convert back to itk, data is not copied itk_np_copy = itk.GetImageFromArray(np_copy) # Save result itk.imwrite(itk_np_view, output_filename) # Vnl matrix[View] from array arr = np.zeros([3, 3], np.uint8) matrix_view = itk.GetVnlMatrixViewFromArray(arr) matrix = itk.GetVnlMatrixFromArray(arr) # Array[View] from Vnl matrix arr_view = itk.GetArrayViewFromVnlMatrix(matrix) arr = itk.GetArrayFromVnlMatrix(matrix)
def main(main_args):
print(os.path.basename(sys.argv[0])+"\n")
### Inputs
T1 = main_args.t1
T2 = main_args.t2
BRAIN_MASK = main_args.brainMask
T2_exists = True
if (T2 == ""):
T2_exists = False
T1_base = os.path.splitext(os.path.splitext(os.path.basename(T1))[0])[0]
if (T2_exists):
T2_base = os.path.splitext(os.path.splitext(T2)[0])[0]
SEGMENTATION = main_args.tissueSeg
### Executables
python = main_args.python3
ImageMath = main_args.ImageMath
ImageStat = main_args.ImageStat
ABC = main_args.ABC
### Masks
CSFLabel = int(main_args.CSFLabel)
### Output path
OUT_PATH = main_args.output
### Scripts
scripts_prefix = os.path.join(OUT_PATH, "PythonScripts")
current_suffix = ""
registration_suffix = "_stx"
if (main_args.registration):
rigid_align_script = os.path.join(scripts_prefix, "rigid_align.py")
print("Running " + rigid_align_script)
call_and_print([python, rigid_align_script])
if (main_args.skullStripping):
make_mask_script = os.path.join(scripts_prefix, "make_mask.py")
print("Running " + make_mask_script)
call_and_print([python, make_mask_script])
skull_strip_script = os.path.join(scripts_prefix, "skull_strip.py")
print("Running " + skull_strip_script)
call_and_print([python, skull_strip_script])
if (main_args.segmentation):
tissue_seg_script = os.path.join(scripts_prefix, "tissue_seg.py")
print("Running " + tissue_seg_script)
call_and_print([python, tissue_seg_script])
abc_seg = find_file(".*_labels_EMS.nrrd", os.path.join(OUT_PATH, "ABC_Segmentation"))
if(abc_seg is not None):
SEGMENTATION = abc_seg
if (main_args.removeVentricles):
vent_mask_script = os.path.join(scripts_prefix, "vent_mask.py")
print("Running " + vent_mask_script)
call_and_print([python, vent_mask_script])
ventricleMasking_seg = find_file(".*_withoutVent.nrrd", os.path.join(OUT_PATH, "VentricleMasking"))
if(ventricleMasking_seg is not None):
SEGMENTATION = ventricleMasking_seg
print("Finished running "+ vent_mask_script)
OUT_FM = os.path.join(OUT_PATH, 'FinalMasking')
if not os.path.exists(OUT_FM):
os.makedirs(OUT_FM)
if(not os.path.exists(BRAIN_MASK)):
BRAIN_MASK = find_file(".*_FinalBrainMask.nrrd", os.path.join(OUT_PATH, "SkullStripping"))
# BRAIN_MASK = os.path.join(OUT_PATH, 'FinalMasking', os.path.splitext(os.path.basename(SEGMENTATION))[0] + "_brainMask.nrrd")
# args=[ImageMath, SEGMENTATION, '-outfile', BRAIN_MASK, '-threshold', "1,999999"]
# call_and_print(args)
BRAIN_MASK_base = os.path.splitext(os.path.basename(BRAIN_MASK))[0]
Segmentation_base = os.path.splitext(os.path.basename(SEGMENTATION))[0]
######### Stripping the skull from segmentation ######
MID_TEMP00 = os.path.join(OUT_FM, "".join([T1_base,"_MID00.nrrd"]))
args=[ImageMath, SEGMENTATION, '-outfile', MID_TEMP00, '-mask', BRAIN_MASK]
call_and_print(args)
######### Cutting below AC-PC line #######
### Coronal mask creation
print("Cutting below AC-PC line")
T1_REGISTERED = T1
if main_args.registration:
T1_REGISTERED = os.path.join(OUT_PATH, "RigidRegistration", T1_base + "_stx.nrrd")
T1_base = os.path.splitext(os.path.splitext(os.path.basename(T1_REGISTERED))[0])[0]
T1_STRIPPED = find_file(T1_base + "_stripped.nrrd", os.path.join(OUT_PATH, "SkullStripping"))
if(T1_STRIPPED is not None):
T1_REGISTERED = T1_STRIPPED
T1_base = os.path.splitext(os.path.splitext(os.path.basename(T1_REGISTERED))[0])[0]
if (main_args.cerebellumMask == ""):
ACPC_unit=main_args.ACPCunit
if(ACPC_unit == "index"):
ACPC_val=int(main_args.ACPCval)
else:
ACPC_mm=float(main_args.ACPCval)
im=itk.imread(T1_REGISTERED)
index_coord=im.TransformPhysicalPointToContinuousIndex([ACPC_mm,0,0])
ACPC_val=round(index_coord[0])
Coronal_Mask = os.path.join(OUT_FM,"coronal_mask_"+str(ACPC_val)+".nrrd")
if not (os.path.isfile(Coronal_Mask)):
im=itk.imread(T1_REGISTERED)
np_copy=itk.GetArrayFromImage(im)
if ((ACPC_val >= np_copy.shape[0]) | (ACPC_val <= 0)):
eprint("ACPC index out of range ("+str(ACPC_val)+"), using default coronal mask (slice 70)")
sys.stderr.flush()
ACPC_val = 70;
print('Creating coronal mask')
np_copy[ACPC_val-1:np_copy.shape[0]-1,:,:]=1
np_copy[0:ACPC_val-1,:,:]=0
itk_np_copy=itk.GetImageViewFromArray(np_copy)
itk_np_copy.SetOrigin(im.GetOrigin())
itk_np_copy.SetSpacing(im.GetSpacing())
itk_np_copy.SetDirection(im.GetDirection())
itk.imwrite(itk_np_copy,Coronal_Mask)
print('Coronal mask created')
else:
print('Loading ' + Coronal_Mask)
else:
Coronal_Mask = main_args.cerebellumMask
### Mask multiplication
MID_TEMP01 = os.path.join(OUT_FM,"".join([Segmentation_base,"_MID01.nrrd"]))
MID_TEMP02 = os.path.join(OUT_FM,"".join([Segmentation_base,"_MID02.nrrd"]))
MID_TEMP03 = os.path.join(OUT_FM,"".join([Segmentation_base,"_MID03.nrrd"]))
args=[ImageMath, MID_TEMP00, '-outfile', MID_TEMP01, '-mask', Coronal_Mask]
call_and_print(args)
args=[ImageMath, MID_TEMP01, '-outfile', MID_TEMP02, '-extractLabel', '3']
call_and_print(args)
args=[ImageMath, MID_TEMP02, '-outfile', MID_TEMP03, '-conComp','1']
call_and_print(args)
Erosion_Mask = os.path.join(OUT_FM,BRAIN_MASK_base + '_Erosion.nrrd')
Erosion_Mask00 = os.path.join(OUT_FM,BRAIN_MASK_base + '_Erosion00.nrrd')
Erosion_Mask01 = os.path.join(OUT_FM,BRAIN_MASK_base + '_Erosion01.nrrd')
Erosion_Mask01_inv = os.path.join(OUT_FM,BRAIN_MASK_base + '_Erosion01_inv.nrrd')
args = [ImageMath, BRAIN_MASK, '-erode', '1,1', '-outfile', Erosion_Mask00]
call_and_print(args)
args = [ImageMath, Erosion_Mask00, '-erode', '1,1', '-outfile', Erosion_Mask01]
call_and_print(args)
for i in range(0,14):
args = [ImageMath, Erosion_Mask01, '-erode', '1,1', '-outfile', Erosion_Mask01]
call_and_print(args)
args = [ImageMath, Erosion_Mask01, '-threshold', '0,0', '-outfile', Erosion_Mask01_inv]
call_and_print(args)
FINAL_CSF_SEG = os.path.join(OUT_FM, Segmentation_base + "_Partial_CSF.nrrd")
args = [ImageMath, MID_TEMP03, '-mask', Erosion_Mask01_inv, '-outfile', FINAL_CSF_SEG]
call_and_print(args)
## add script erase quad....
Erosion = os.path.join(OUT_FM, Segmentation_base + "Partial_Ero.nrrd")
Erosion_MASKING = os.path.join(OUT_FM,Segmentation_base + "Partial_Masking.nrrd")
args = [ImageMath, FINAL_CSF_SEG, '-erode', '1,1', '-outfile', Erosion]
call_and_print(args)
args = [ImageMath, Erosion_Mask01, '-erode', '1,1', '-outfile', Erosion_Mask]
call_and_print(args)
for i in range(0,5):
args = [ImageMath, Erosion_Mask, '-erode', '1,1', '-outfile', Erosion_Mask]
call_and_print(args)
args = [ImageMath, Erosion, '-mask', Erosion_Mask, '-outfile', Erosion_MASKING]
call_and_print(args)
Erosion_MASKING_conComp = os.path.join(OUT_FM, Segmentation_base + "Partial_conComp.nrrd")
args = [ImageMath, Erosion_MASKING, '-conComp', '1', '-outfile', Erosion_MASKING_conComp]
call_and_print(args)
Dilation_comComp = os.path.join(OUT_FM, Segmentation_base + "Partial_conComp_dil.nrrd")
args = [ImageMath, Erosion_MASKING_conComp, '-dilate', '1,1', '-outfile', Dilation_comComp]
call_and_print(args)
for k in range(0,13):
args = [ImageMath, Dilation_comComp, '-dilate', '1,1', '-outfile', Dilation_comComp]
call_and_print(args)
FINAL_RESULT = os.path.join(OUT_PATH, Segmentation_base + "_FINAL_QCistern.nrrd")
args = [ImageMath, Dilation_comComp, '-threshold', '0,0', '-outfile', Dilation_comComp]
call_and_print(args)
args = [ImageMath, FINAL_CSF_SEG, '-mask', Dilation_comComp, '-outfile', FINAL_RESULT]
call_and_print(args)
args = [ImageStat, FINAL_RESULT, '-label', FINAL_RESULT, '-volumeSummary']
call_and_print(args)
if (main_args.computeCSFDensity == "true"):
LH_INNER_SURF = main_args.LHInnerSurf
RH_INNER_SURF = main_args.RHInnerSurf
OUT_CCD=os.path.join(OUT_PATH,'CSF_Density')
args = ['computecsfdensity', '-l', LH_INNER_SURF, '-r', RH_INNER_SURF, '-s', Segmentation, '-c', FINAL_OUTERCSF, '-o', OUT_CCD, '-p', Segmentation_base]
call_and_print(args)
print("Auto_EACSF finished")
sys.exit(0);
def main(main_args):
print("<b>>>> Running "+os.path.basename(sys.argv[0])+"</b>\n")
sys.stdout.flush()
### Inputs
T1 = main_args.t1
T2 = main_args.t2
T2_exists=True
if (T2 == ""):
T2_exists=False
T1_split=os.path.splitext(os.path.basename(T1))
if (T1_split[1] == 'gz'):
T1_base=os.path.splitext(T1_split[0])
else:
T1_base=T1_split[0]
if (T2_exists):
T2_split=os.path.splitext(os.path.basename(T2))
if (T2_split[1] == 'gz'):
T2_base=os.path.splitext(T2_split[0])
else:
T2_base=T2_split[0]
### Data directory
DATADIR = main_args.dataDir
### Coronal mask settings
ACPC_unit=main_args.ACPCunit
if(ACPC_unit == "index"):
ACPC_val=int(main_args.ACPCval)
else:
ACPC_mm=float(main_args.ACPCval)
im=itk.imread(T1)
index_coord=im.TransformPhysicalPointToContinuousIndex([ACPC_mm,0,0])
ACPC_val=round(index_coord[0])
if(ACPC_val == 70):
Coronal_Mask = os.path.join(DATADIR,"masks","coronal_mask_70.nrrd")
else:
im=itk.imread(T1)
np_copy=itk.GetArrayFromImage(im)
if ((ACPC_val >= np_copy.shape[0]) | (ACPC_val <= 0)):
eprint("ACPC index out of range ("+str(ACPC_val)+"), using default coronal mask (slice 70)")
sys.stderr.flush()
Coronal_Mask = os.path.join(DATADIR,"masks","coronal_mask_70.nrrd")
else:
np_copy[ACPC_val-1:np_copy.shape[0]-1,:,:]=np.iinfo(np_copy.dtype).max
np_copy[0:ACPC_val-1,:,:]=0
itk_np_copy=itk.GetImageViewFromArray(np_copy)
itk_outfile_name="coronal_mask_"+str(ACPC_val)
itk.imwrite(itk_np_copy,itk_outfile_name)
Coronal_Mask = itk_outfile_name
### Executables
python=main_args.python3
ImageMath=main_args.ImageMath
ImageStat=main_args.ImageStat
ABC=main_args.ABC
### Masks
BRAIN_MASK = main_args.brainMask
if (main_args.useDfCerMask == "true"):
PRE_CEREBELLUM_MASK = "/work/alemaout/sources/Projects/auto_EACSF-Project/auto_EACSF/data/CVS_MASK_RAI_Dilate.nrrd"
else:
PRE_CEREBELLUM_MASK = main_args.cerebellumMask
Segmentation = main_args.tissueSeg
if (Segmentation == "") :
CSFLabel=3
else:
CSFLabel=int(main_args.CSFLabel)
### Output path
OUT_PATH = main_args.output
### Scripts
scripts_prefix="PythonScripts/"
if (main_args.performReg == "true"):
rigid_align="rigid_align_script.py"
print_main_info("Running "+rigid_align)
sys.stdout.flush()
OUT_RR=os.path.join(OUT_PATH,'RigidRegistration')
call([python, scripts_prefix+rigid_align,'--output',OUT_RR])
T1 = os.path.join(OUT_RR, "".join([T1_base,"_stx.nrrd"]))
if (T2_exists):
T2=os.path.join(OUT_RR, "".join([T2_base,"_stx.nrrd"]))
print_main_info("Finished running "+rigid_align)
sys.stdout.flush()
if (main_args.performSS == "true"):
make_mask="make_mask_script.py"
print_main_info("Running "+make_mask)
sys.stdout.flush()
OUT_SS=os.path.join(OUT_PATH,'SkullStripping')
call([python, scripts_prefix+make_mask, '--t1', T1, '--t2', T2, '--at_dir', '--at_list', '--output',OUT_SS])
BRAIN_MASK = os.path.join(OUT_SS, "".join([T1_base,"_FinalBrainMask.nrrd"]))
print_main_info("Finished running "+make_mask)
sys.stdout.flush()
if (main_args.performTSeg == "true"):
tissue_seg="tissue_seg_script.py"
print_main_info("Running tissue_seg_script.py")
sys.stdout.flush()
OUT_TS=os.path.join(OUT_PATH,'TissueSegAtlas')
OUT_ABC=os.path.join(OUT_PATH,'ABC_Segmentation')
call([python, scripts_prefix+tissue_seg, '--t1', T1, '--t2', T2,'--at_dir', '--output', OUT_TS])
print_main_info("Finished running tissue_seg_script.py")
Segmentation = os.path.join(OUT_ABC, "".join([T1_base,"_labels_EMS.nrrd"]))
sys.stdout.flush()
vent_mask="vent_mask_script.py"
print_main_info("Running "+vent_mask)
sys.stdout.flush()
OUT_VR=os.path.join(OUT_PATH,'VentricleMasking')
call([python, scripts_prefix+vent_mask, '--t1', T1, '--tissueSeg', Segmentation, '--atlas', '--output',OUT_VR])
Segmentation = os.path.join(OUT_VR, "".join([T1_base,"_EMS_withoutVent.nrrd"]))
print_main_info("Finished running "+vent_mask)
sys.stdout.flush()
BRAIN_MASK_dir = os.path.dirname(BRAIN_MASK)
BRAIN_MASK_base = os.path.splitext(os.path.basename(BRAIN_MASK))[0]
PRE_CEREBELLUM_MASK_dir = os.path.dirname(PRE_CEREBELLUM_MASK)
PRE_CEREBELLUM_MASK_base = os.path.splitext(os.path.basename(PRE_CEREBELLUM_MASK))[0]
Segmentation_dir = os.path.dirname(Segmentation)
Segmentation_base = os.path.splitext(os.path.basename(Segmentation))[0]
######### Stripping the skull ######
print_main_info("Stripping the skull")
MID_TEMP00 = os.path.join(OUT_PATH, "".join([T1_base,"_MID00.nrrd"]))
args=[ImageMath, Segmentation, '-outfile', MID_TEMP00, '-mul', BRAIN_MASK]
call_and_print(args)
######### Cutting below AC-PC line #######
print_main_info("Cutting below AC-PC line")
MID_TEMP01 = os.path.join(OUT_PATH,"".join([Segmentation_base,"_MID01.nrrd"]))
MID_TEMP02 = os.path.join(OUT_PATH,"".join([Segmentation_base,"_MID02.nrrd"]))
MID_TEMP03 = os.path.join(OUT_PATH,"".join([Segmentation_base,"_MID03.nrrd"]))
args=[ImageMath, MID_TEMP00, '-outfile', MID_TEMP01, '-mul', Coronal_Mask]
call_and_print(args)
args=[ImageMath, MID_TEMP01, '-outfile', MID_TEMP02, '-extractLabel', '3']
call_and_print(args)
args=[ImageMath, MID_TEMP02, '-outfile', MID_TEMP02, '-sub', PRE_CEREBELLUM_MASK]
call_and_print(args)
args=[ImageMath, MID_TEMP02, '-outfile', MID_TEMP02, '-threshold', '1,1']
call_and_print(args)
args=[ImageMath, MID_TEMP02, '-outfile', MID_TEMP03, '-conComp','1']
call_and_print(args)
######### Make thin mask for preserving outter 2nd or 3rd fracrtion csf ##########
print_main_info("Make thin mask for preserving outter 2nd or 3rd fracrtion csf")
BRAIN_MASK = os.path.join(OUT_PATH,'Brain_Mask.nrrd')
BRAIN_MASK_ERODE = os.path.join(OUT_PATH,'Brain_Mask_Erode.nrrd')
THIN_MASK = os.path.join(OUT_PATH,'Brain_Thin_Mask.nrrd')
args=[ImageMath, Segmentation, '-threshold', '1,4', '-outfile', BRAIN_MASK]
call_and_print(args)
args=[ImageMath, BRAIN_MASK, '-erode', '6,1', '-outfile', BRAIN_MASK_ERODE]
call_and_print(args)
args=[ImageMath, BRAIN_MASK, '-sub', BRAIN_MASK_ERODE, '-outfile', THIN_MASK]
call_and_print(args)
args=[ImageMath, THIN_MASK, '-mul', Coronal_Mask, '-outfile', THIN_MASK]
call_and_print(args)
########## Find outer CSF in thin mask and added this csf to extra CSF #############
print_main_info("Find outer CSF in thin mask and added this csf to extra CSF")
PRESERVED_OUTERCSF = os.path.join(OUT_PATH,'Preserved_OuterCSF.nrrd')
FINAL_OUTERCSF = os.path.join(OUT_PATH, "".join([Segmentation_base,"_extCSF.nrrd"]))
FINAL_OUTERCSF_dir = os.path.dirname(FINAL_OUTERCSF)
FINAL_OUTERCSF_base = os.path.splitext(os.path.basename(FINAL_OUTERCSF))[0]
args=[ImageMath, MID_TEMP02, '-outfile', PRESERVED_OUTERCSF, '-mul', THIN_MASK]
call_and_print(args)
args=[ImageMath, MID_TEMP03, '-outfile', FINAL_OUTERCSF, '-add', PRESERVED_OUTERCSF]
call_and_print(args)
args=[ImageMath, FINAL_OUTERCSF, '-outfile', FINAL_OUTERCSF, '-threshold', '1,2']
call_and_print(args)
args=[ImageStat, FINAL_OUTERCSF, '-label', FINAL_OUTERCSF]
call_and_print(args)
print_main_info("Auto_EACSF finished")
sys.exit(0);
