def use_viewer_to_define_labels(fname_data, first_label, nb_of_slices_to_mean):
from sct_viewer import ClickViewerGroundTruth
from msct_image import Image
import sct_image
image_input = Image(fname_data)
image_input_orientation = sct_image.orientation(image_input,
get=True,
verbose=False)
reoriented_image_filename = 'reoriented_image_source.nii.gz'
path_tmp_viewer = sct.tmp_create(verbose=False)
cmd_image = 'sct_image -i "%s" -o "%s" -setorient SAL -v 0' % (
fname_data, reoriented_image_filename)
sct.run(cmd_image, verbose=False)
im_input_SAL = prepare_input_image_for_viewer(fname_data)
viewer = ClickViewerGroundTruth(im_input_SAL,
first_label,
orientation_subplot=['sag', 'ax'])
set_viewer_parameters(viewer, nb_of_slices_to_mean)
mask_points = viewer.start()
if not mask_points and viewer.closed:
mask_points = viewer.list_points_useful_notation
make_labels_image_from_list_points(mask_points, reoriented_image_filename,
image_input_orientation)
def prepare(list_images):
fname_images, orientation_images = [], []
for fname_im in list_images:
from sct_image import orientation
orientation_images.append(orientation(Image(fname_im), get=True, verbose=False))
path_fname, file_fname, ext_fname = sct.extract_fname(fname_im)
reoriented_image_filename = 'tmp.' + sct.add_suffix(file_fname + ext_fname, "_SAL")
sct.run('sct_image -i ' + fname_im + ' -o ' + reoriented_image_filename + ' -setorient SAL -v 0', verbose=False)
fname_images.append(reoriented_image_filename)
return fname_images, orientation_images
def check_and_correct_segmentation(fname_segmentation,
fname_centerline,
folder_output='',
threshold_distance=5.0,
remove_temp_files=1,
verbose=0):
"""
This function takes the outputs of isct_propseg (centerline and segmentation) and check if the centerline of the
segmentation is coherent with the centerline provided by the isct_propseg, especially on the edges (related
to issue #1074).
Args:
fname_segmentation: filename of binary segmentation
fname_centerline: filename of binary centerline
threshold_distance: threshold, in mm, beyond which centerlines are not coherent
verbose:
Returns: None
"""
sct.printv('\nCheck consistency of segmentation...', verbose)
# creating a temporary folder in which all temporary files will be placed and deleted afterwards
path_tmp = sct.tmp_create(basename="propseg", verbose=verbose)
from sct_convert import convert
convert(fname_segmentation,
os.path.join(path_tmp, "tmp.segmentation.nii.gz"),
squeeze_data=False,
verbose=0)
convert(fname_centerline,
os.path.join(path_tmp, "tmp.centerline.nii.gz"),
squeeze_data=False,
verbose=0)
fname_seg_absolute = os.path.abspath(fname_segmentation)
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# convert segmentation image to RPI
im_input = Image('tmp.segmentation.nii.gz')
image_input_orientation = orientation(im_input, get=True, verbose=False)
sct_image.main(
"-i tmp.segmentation.nii.gz -setorient RPI -o tmp.segmentation_RPI.nii.gz -v 0"
.split())
sct_image.main(
"-i tmp.centerline.nii.gz -setorient RPI -o tmp.centerline_RPI.nii.gz -v 0"
.split())
# go through segmentation image, and compare with centerline from propseg
im_seg = Image('tmp.segmentation_RPI.nii.gz')
im_centerline = Image('tmp.centerline_RPI.nii.gz')
# Get size of data
sct.printv('\nGet data dimensions...', verbose)
nx, ny, nz, nt, px, py, pz, pt = im_seg.dim
# extraction of centerline provided by isct_propseg and computation of center of mass for each slice
# the centerline is defined as the center of the tubular mesh outputed by propseg.
centerline, key_centerline = {}, []
for i in range(nz):
slice = im_centerline.data[:, :, i]
if np.any(slice):
x_centerline, y_centerline = ndi.measurements.center_of_mass(slice)
centerline[str(i)] = [x_centerline, y_centerline]
key_centerline.append(i)
minz_centerline = np.min(key_centerline)
maxz_centerline = np.max(key_centerline)
mid_slice = int((maxz_centerline - minz_centerline) / 2)
# for each slice of the segmentation, check if only one object is present. If not, remove the slice from segmentation.
# If only one object (the spinal cord) is present in the slice, check if its center of mass is close to the centerline of isct_propseg.
slices_to_remove = [
False
] * nz # flag that decides if the slice must be removed
for i in range(minz_centerline, maxz_centerline + 1):
# extraction of slice
slice = im_seg.data[:, :, i]
distance = -1
label_objects, nb_labels = ndi.label(
slice) # count binary objects in the slice
if nb_labels > 1: # if there is more that one object in the slice, the slice is removed from the segmentation
slices_to_remove[i] = True
elif nb_labels == 1: # check if the centerline is coherent with the one from isct_propseg
x_centerline, y_centerline = ndi.measurements.center_of_mass(slice)
slice_nearest_coord = min(key_centerline, key=lambda x: abs(x - i))
coord_nearest_coord = centerline[str(slice_nearest_coord)]
distance = np.sqrt((
(x_centerline - coord_nearest_coord[0]) * px)**2 + (
(y_centerline - coord_nearest_coord[1]) * py)**2 +
((i - slice_nearest_coord) * pz)**2)
if distance >= threshold_distance: # threshold must be adjusted, default is 5 mm
slices_to_remove[i] = True
# Check list of removal and keep one continuous centerline (improve this comment)
# Method:
# starting from mid-centerline (in both directions), the first True encountered is applied to all following slices
slice_to_change = False
for i in range(mid_slice, nz):
if slice_to_change:
slices_to_remove[i] = True
elif slices_to_remove[i]:
slice_to_change = True
slice_to_change = False
for i in range(mid_slice, 0, -1):
if slice_to_change:
slices_to_remove[i] = True
elif slices_to_remove[i]:
slice_to_change = True
for i in range(0, nz):
# remove the slice
if slices_to_remove[i]:
im_seg.data[:, :, i] *= 0
# saving the image
im_seg.setFileName('tmp.segmentation_RPI_c.nii.gz')
im_seg.save()
# replacing old segmentation with the corrected one
sct_image.main(
'-i tmp.segmentation_RPI_c.nii.gz -setorient {} -o {} -v 0'.format(
image_input_orientation, fname_seg_absolute).split())
os.chdir(curdir)
# display information about how much of the segmentation has been corrected
# remove temporary files
if remove_temp_files:
# sct.printv("\nRemove temporary files...", verbose)
sct.rmtree(path_tmp)
def main():
# initialization
fname_mask = ''
# Get parser info
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
fname_data = arguments['-i']
fname_mask = arguments['-m']
vert_label_fname = arguments["-vertfile"]
vert_levels = arguments["-vert"]
slices_of_interest = arguments["-z"]
index_vol = arguments['-vol']
method = arguments["-method"]
remove_temp_files = int(arguments['-r'])
verbose = int(arguments['-v'])
# Check if data are in RPI
input_im = Image(fname_data)
input_orient = get_orientation(input_im)
# If orientation is not RPI, change to RPI
if input_orient != 'RPI':
sct.printv(
'\nCreate temporary folder to change the orientation of the NIFTI files into RPI...',
verbose)
path_tmp = sct.tmp_create()
# change orientation and load data
sct.printv('\nChange input image orientation and load it...', verbose)
input_im_rpi = orientation(input_im,
ori='RPI',
set=True,
fname_out=os.path.join(
path_tmp, "input_RPI.nii"))
input_data = input_im_rpi.data
# Do the same for the mask
sct.printv('\nChange mask orientation and load it...', verbose)
mask_im_rpi = orientation(Image(fname_mask),
ori='RPI',
set=True,
fname_out=os.path.join(
path_tmp, "mask_RPI.nii"))
mask_data = mask_im_rpi.data
# Do the same for vertebral labeling if present
if vert_levels != 'None':
sct.printv(
'\nChange vertebral labeling file orientation and load it...',
verbose)
vert_label_im_rpi = orientation(Image(vert_label_fname),
ori='RPI',
set=True,
fname_out=os.path.join(
path_tmp,
"vert_labeling_RPI.nii"))
vert_labeling_data = vert_label_im_rpi.data
# Remove the temporary folder used to change the NIFTI files orientation into RPI
if remove_temp_files:
sct.printv('\nRemove the temporary folder...', verbose)
sct.rmtree(path_tmp, True)
else:
# Load data
sct.printv('\nLoad data...', verbose)
input_data = input_im.data
mask_data = Image(fname_mask).data
if vert_levels != 'None':
vert_labeling_data = Image(vert_label_fname).data
sct.printv('\tDone.', verbose)
# Get slices corresponding to vertebral levels
if vert_levels != 'None':
from sct_extract_metric import get_slices_matching_with_vertebral_levels
slices_of_interest, actual_vert_levels, warning_vert_levels = get_slices_matching_with_vertebral_levels(
mask_data, vert_levels, vert_labeling_data, verbose)
# Remove slices that were not selected
if slices_of_interest == 'None':
slices_of_interest = '0:' + str(mask_data.shape[2] - 1)
slices_boundary = slices_of_interest.split(':')
slices_of_interest_list = range(int(slices_boundary[0]),
int(slices_boundary[1]) + 1)
# Crop
input_data = input_data[:, :, slices_of_interest_list, :]
mask_data = mask_data[:, :, slices_of_interest_list]
# if user selected all slices (-vol -1), then assign index_vol
if index_vol[0] == -1:
index_vol = range(0, input_data.shape[3], 1)
# Get signal and noise
indexes_roi = np.where(mask_data == 1)
if method == 'mult':
# get voxels in ROI to obtain a (x*y*z)*t 2D matrix
input_data_in_roi = input_data[indexes_roi]
# compute signal and STD across by averaging across time
signal = np.mean(input_data_in_roi[:, index_vol])
std_input_temporal = np.std(input_data_in_roi[:, index_vol], 1)
noise = np.mean(std_input_temporal)
elif method == 'diff':
# if user did not select two volumes, then exit with error
if not len(index_vol) == 2:
sct.printv(
'ERROR: ' + str(len(index_vol)) +
' volumes were specified. Method "diff" should be used with exactly two volumes.',
1, 'error')
data_1 = input_data[:, :, :, index_vol[0]]
data_2 = input_data[:, :, :, index_vol[1]]
# compute voxel-average of voxelwise sum
signal = np.mean(np.add(data_1[indexes_roi], data_2[indexes_roi]))
# compute voxel-STD of voxelwise substraction, multiplied by sqrt(2) as described in equation 7 of Dietrich et al.
noise = np.std(np.subtract(data_1[indexes_roi],
data_2[indexes_roi])) * np.sqrt(2)
# compute SNR
SNR = signal / noise
# Display result
sct.printv('\nSNR_' + method + ' = ' + str(SNR) + '\n', type='info')
def run_main():
sct.start_stream_logger()
parser = get_parser()
args = sys.argv[1:]
arguments = parser.parse(args)
# Input filename
fname_input_data = arguments["-i"]
fname_data = os.path.abspath(fname_input_data)
# Method used
method = 'optic'
if "-method" in arguments:
method = arguments["-method"]
# Contrast type
contrast_type = ''
if "-c" in arguments:
contrast_type = arguments["-c"]
if method == 'optic' and not contrast_type:
# Contrast must be
error = 'ERROR: -c is a mandatory argument when using Optic method.'
sct.printv(error, type='error')
return
# Ga between slices
interslice_gap = 10.0
if "-gap" in arguments:
interslice_gap = float(arguments["-gap"])
# Output folder
if "-ofolder" in arguments:
folder_output = sct.slash_at_the_end(arguments["-ofolder"], slash=1)
else:
folder_output = './'
# Remove temporary files
remove_temp_files = True
if "-r" in arguments:
remove_temp_files = bool(int(arguments["-r"]))
# Outputs a ROI file
output_roi = False
if "-roi" in arguments:
output_roi = bool(int(arguments["-roi"]))
# Verbosity
verbose = 0
if "-v" in arguments:
verbose = int(arguments["-v"])
if method == 'viewer':
path_data, file_data, ext_data = sct.extract_fname(fname_data)
# create temporary folder
temp_folder = sct.TempFolder()
temp_folder.copy_from(fname_data)
temp_folder.chdir()
# make sure image is in SAL orientation, as it is the orientation used by the viewer
image_input = Image(fname_data)
image_input_orientation = orientation(image_input,
get=True,
verbose=False)
reoriented_image_filename = sct.add_suffix(file_data + ext_data,
"_SAL")
cmd_image = 'sct_image -i "%s" -o "%s" -setorient SAL -v 0' % (
fname_data, reoriented_image_filename)
sct.run(cmd_image, verbose=False)
# extract points manually using the viewer
fname_points = viewer_centerline(image_fname=reoriented_image_filename,
interslice_gap=interslice_gap,
verbose=verbose)
if fname_points is not None:
image_points_RPI = sct.add_suffix(fname_points, "_RPI")
cmd_image = 'sct_image -i "%s" -o "%s" -setorient RPI -v 0' % (
fname_points, image_points_RPI)
sct.run(cmd_image, verbose=False)
image_input_reoriented = Image(image_points_RPI)
# fit centerline, smooth it and return the first derivative (in physical space)
x_centerline_fit, y_centerline_fit, z_centerline, x_centerline_deriv, y_centerline_deriv, z_centerline_deriv = smooth_centerline(
image_points_RPI,
algo_fitting='nurbs',
nurbs_pts_number=3000,
phys_coordinates=True,
verbose=verbose,
all_slices=False)
centerline = Centerline(x_centerline_fit, y_centerline_fit,
z_centerline, x_centerline_deriv,
y_centerline_deriv, z_centerline_deriv)
# average centerline coordinates over slices of the image
x_centerline_fit_rescorr, y_centerline_fit_rescorr, z_centerline_rescorr, x_centerline_deriv_rescorr, y_centerline_deriv_rescorr, z_centerline_deriv_rescorr = centerline.average_coordinates_over_slices(
image_input_reoriented)
# compute z_centerline in image coordinates for usage in vertebrae mapping
voxel_coordinates = image_input_reoriented.transfo_phys2pix([[
x_centerline_fit_rescorr[i], y_centerline_fit_rescorr[i],
z_centerline_rescorr[i]
] for i in range(len(z_centerline_rescorr))])
x_centerline_voxel = [coord[0] for coord in voxel_coordinates]
y_centerline_voxel = [coord[1] for coord in voxel_coordinates]
z_centerline_voxel = [coord[2] for coord in voxel_coordinates]
# compute z_centerline in image coordinates with continuous precision
voxel_coordinates = image_input_reoriented.transfo_phys2continuouspix(
[[
x_centerline_fit_rescorr[i], y_centerline_fit_rescorr[i],
z_centerline_rescorr[i]
] for i in range(len(z_centerline_rescorr))])
x_centerline_voxel_cont = [coord[0] for coord in voxel_coordinates]
y_centerline_voxel_cont = [coord[1] for coord in voxel_coordinates]
z_centerline_voxel_cont = [coord[2] for coord in voxel_coordinates]
# Create an image with the centerline
image_input_reoriented.data *= 0
min_z_index, max_z_index = int(round(
min(z_centerline_voxel))), int(round(max(z_centerline_voxel)))
for iz in range(min_z_index, max_z_index + 1):
image_input_reoriented.data[
int(round(x_centerline_voxel[iz - min_z_index])),
int(round(y_centerline_voxel[iz - min_z_index])),
int(
iz
)] = 1 # if index is out of bounds here for hanning: either the segmentation has holes or labels have been added to the file
# Write the centerline image
sct.printv('\nWrite NIFTI volumes...', verbose)
fname_centerline_oriented = file_data + '_centerline' + ext_data
image_input_reoriented.setFileName(fname_centerline_oriented)
image_input_reoriented.changeType('uint8')
image_input_reoriented.save()
sct.printv('\nSet to original orientation...', verbose)
sct.run('sct_image -i ' + fname_centerline_oriented +
' -setorient ' + image_input_orientation + ' -o ' +
fname_centerline_oriented)
# create a txt file with the centerline
fname_centerline_oriented_txt = file_data + '_centerline.txt'
file_results = open(fname_centerline_oriented_txt, 'w')
for i in range(min_z_index, max_z_index + 1):
file_results.write(
str(int(i)) + ' ' +
str(round(x_centerline_voxel_cont[i - min_z_index], 2)) +
' ' +
str(round(y_centerline_voxel_cont[i - min_z_index], 2)) +
'\n')
file_results.close()
fname_centerline_oriented_roi = optic.centerline2roi(
fname_image=fname_centerline_oriented,
folder_output='./',
verbose=verbose)
# return to initial folder
temp_folder.chdir_undo()
# copy result to output folder
shutil.copy(temp_folder.get_path() + fname_centerline_oriented,
folder_output)
shutil.copy(temp_folder.get_path() + fname_centerline_oriented_txt,
folder_output)
if output_roi:
shutil.copy(
temp_folder.get_path() + fname_centerline_oriented_roi,
folder_output)
centerline_filename = folder_output + fname_centerline_oriented
else:
centerline_filename = 'error'
# delete temporary folder
if remove_temp_files:
temp_folder.cleanup()
else:
# condition on verbose when using OptiC
if verbose == 1:
verbose = 2
# OptiC models
path_script = os.path.dirname(__file__)
path_sct = os.path.dirname(path_script)
optic_models_path = os.path.join(path_sct, 'data/optic_models',
'{}_model'.format(contrast_type))
# Execute OptiC binary
_, centerline_filename = optic.detect_centerline(
image_fname=fname_data,
contrast_type=contrast_type,
optic_models_path=optic_models_path,
folder_output=folder_output,
remove_temp_files=remove_temp_files,
output_roi=output_roi,
verbose=verbose)
sct.printv('\nDone! To view results, type:', verbose)
sct.printv(
"fslview " + fname_input_data + " " + centerline_filename +
" -l Red -b 0,1 -t 0.7 &\n", verbose, 'info')
def detect_centerline(image_fname,
contrast_type,
optic_models_path,
folder_output,
remove_temp_files=False,
init_option=None,
output_roi=False,
verbose=0):
"""This method will use the OptiC to detect the centerline.
:param image_fname: The input image filename.
:param init_option: Axial slice where the propagation starts.
:param contrast_type: The contrast type.
:param optic_models_path: The path with the Optic model files.
:param folder_output: The OptiC output folder.
:param remove_temp_files: Remove the temporary created files.
:param verbose: Adjusts the verbosity of the logging.
:returns: The OptiC output filename.
"""
image_input = Image(image_fname)
path_data, file_data, ext_data = sct.extract_fname(image_fname)
sct.printv('Detecting the spinal cord using OptiC', verbose=verbose)
image_input_orientation = orientation(image_input, get=True, verbose=False)
temp_folder = sct.TempFolder()
temp_folder.copy_from(image_fname)
curdir = os.getcwd()
temp_folder.chdir()
# convert image data type to int16, as required by opencv (backend in OptiC)
image_int_filename = sct.add_suffix(file_data + ext_data, "_int16")
img = Image(image_fname)
img_int16 = img.copy()
# rescale intensity
min_out = np.iinfo('uint16').min
max_out = np.iinfo('uint16').max
min_in = np.nanmin(img.data)
max_in = np.nanmax(img.data)
data_rescaled = img.data * (max_out - min_out) / (max_in - min_in)
img_int16.data = data_rescaled - (data_rescaled.min() - min_out)
# change data type
img_int16.changeType('uint16')
img_int16.setFileName(image_int_filename)
img_int16.save()
del img, img_int16
# reorient the input image to RPI + convert to .nii
reoriented_image_filename = sct.add_suffix(image_int_filename, "_RPI")
img_filename = ''.join(sct.extract_fname(reoriented_image_filename)[:2])
reoriented_image_filename_nii = img_filename + '.nii'
cmd_reorient = 'sct_image -i "%s" -o "%s" -setorient RPI -v 0' % \
(image_int_filename, reoriented_image_filename_nii)
sct.run(cmd_reorient, verbose=0)
image_rpi_init = Image(reoriented_image_filename_nii)
nxr, nyr, nzr, ntr, pxr, pyr, pzr, ptr = image_rpi_init.dim
if init_option is not None:
if init_option > 1:
init_option /= (nzr - 1)
# call the OptiC method to generate the spinal cord centerline
optic_input = img_filename
optic_filename = img_filename + '_optic'
os.environ["FSLOUTPUTTYPE"] = "NIFTI_PAIR"
cmd_optic = 'isct_spine_detect -ctype=dpdt -lambda=1 "%s" "%s" "%s"' % \
(optic_models_path, optic_input, optic_filename)
sct.run(cmd_optic, verbose=0)
# convert .img and .hdr files to .nii.gz
optic_hdr_filename = img_filename + '_optic_ctr.hdr'
centerline_optic_RPI_filename = sct.add_suffix(file_data + ext_data,
"_centerline_optic_RPI")
img = nib.load(optic_hdr_filename)
nib.save(img, centerline_optic_RPI_filename)
# reorient the output image to initial orientation
centerline_optic_filename = sct.add_suffix(file_data + ext_data,
"_centerline_optic")
cmd_reorient = 'sct_image -i "%s" -o "%s" -setorient "%s" -v 0' % \
(centerline_optic_RPI_filename,
centerline_optic_filename,
image_input_orientation)
sct.run(cmd_reorient, verbose=0)
# copy centerline to parent folder
folder_output_from_temp = folder_output
if not os.path.isabs(folder_output):
folder_output_from_temp = os.path.join(curdir, folder_output)
sct.printv('Copy output to ' + folder_output, verbose=0)
sct.copy(centerline_optic_filename, folder_output_from_temp)
if output_roi:
fname_roi_centerline = centerline2roi(
fname_image=centerline_optic_RPI_filename,
folder_output=folder_output_from_temp,
verbose=verbose)
# Note: the .roi file is defined in RPI orientation. To be used, it must be applied on the original image with
# a RPI orientation. For this reason, this script also outputs the input image in RPI orientation
sct.copy(reoriented_image_filename_nii, folder_output_from_temp)
# return to initial folder
temp_folder.chdir_undo()
# delete temporary folder
if remove_temp_files:
temp_folder.cleanup()
return init_option, os.path.join(folder_output, centerline_optic_filename)
# check if input image is in 3D. Otherwise itk image reader will cut the 4D image in 3D volumes and only take the first one.
from msct_image import Image
image_input = Image(fname_data)
nx, ny, nz, nt, px, py, pz, pt = image_input.dim
if nt > 1:
sct.printv(
'ERROR: your input image needs to be 3D in order to be segmented.',
1, 'error')
path_data, file_data, ext_data = sct.extract_fname(fname_data)
# if centerline or mask is asked using viewer
if use_viewer:
# make sure image is in SAL orientation, as it is the orientation used by PropSeg
image_input_orientation = orientation(image_input,
get=True,
verbose=False)
reoriented_image_filename = 'tmp.' + sct.add_suffix(
file_data + ext_data, "_SAL")
path_tmp_viewer = sct.tmp_create(verbose=verbose)
cmd_image = 'sct_image -i "%s" -o "%s" -setorient SAL -v 0' % (
fname_data, os.path.join(path_tmp_viewer,
reoriented_image_filename))
sct.run(cmd_image, verbose=False)
from sct_viewer import ClickViewerPropseg
image_input_reoriented = Image(path_tmp_viewer +
reoriented_image_filename)
viewer = ClickViewerPropseg(image_input_reoriented)
if use_viewer == "mask":
viewer.input_type = 'mask'
def check_and_correct_segmentation(fname_segmentation, fname_centerline, threshold_distance=5.0, remove_temp_files=1, verbose=0):
"""
This function takes the outputs of isct_propseg (centerline and segmentation) and check if the centerline of the
segmentation is coherent with the centerline provided by the isct_propseg, especially on the edges (related
to issue #1074).
Args:
fname_segmentation: filename of binary segmentation
fname_centerline: filename of binary centerline
threshold_distance: threshold, in mm, beyond which centerlines are not coherent
verbose:
Returns: None
"""
# creating a temporary folder in which all temporary files will be placed and deleted afterwards
path_tmp = sct.tmp_create(verbose=verbose)
shutil.copy(fname_segmentation, path_tmp + 'tmp.segmentation.nii.gz')
shutil.copy(fname_centerline, path_tmp + 'tmp.centerline.nii.gz')
# go to tmp folder
os.chdir(path_tmp)
# convert segmentation image to RPI
im_input = Image('tmp.segmentation.nii.gz')
image_input_orientation = orientation(im_input, get=True, verbose=False)
sct.run('sct_image -i tmp.segmentation.nii.gz -setorient RPI -o tmp.segmentation_RPI.nii.gz', verbose)
sct.run('sct_image -i tmp.centerline.nii.gz -setorient RPI -o tmp.centerline_RPI.nii.gz', verbose)
# go through segmentation image, and compare with centerline from propseg
im_seg = Image('tmp.segmentation_RPI.nii.gz')
im_centerline = Image('tmp.centerline_RPI.nii.gz')
# Get size of data
sct.printv('\nGet data dimensions...', verbose)
nx, ny, nz, nt, px, py, pz, pt = im_seg.dim
# extraction of centerline provided by isct_propseg and computation of center of mass for each slice
# the centerline is defined as the center of the tubular mesh outputed by propseg.
centerline, key_centerline = {}, []
for i in range(nz):
slice = im_centerline.data[:, :, i]
if np.any(slice):
x_centerline, y_centerline = ndi.measurements.center_of_mass(slice)
centerline[str(i)] = [x_centerline, y_centerline]
key_centerline.append(i)
# for each slice of the segmentation, check if only one object is present. If not, remove the slice from segmentation.
# If only one object (the spinal cord) is present in the slice, check if its center of mass is close to the centerline of isct_propseg.
slices_to_remove = [False] * nz # flag that decides if the slice must be removed
for i in range(nz):
# extraction of slice
slice = im_seg.data[:, :, i]
distance = -1
label_objects, nb_labels = ndi.label(slice) # count binary objects in the slice
if nb_labels > 1: # if there is more that one object in the slice, the slice is removed from the segmentation
slices_to_remove[i] = True
elif nb_labels == 1: # check if the centerline is coherent with the one from isct_propseg
x_centerline, y_centerline = ndi.measurements.center_of_mass(slice)
slice_nearest_coord = min(key_centerline, key=lambda x:abs(x-i))
coord_nearest_coord = centerline[str(slice_nearest_coord)]
distance = np.sqrt(((x_centerline - coord_nearest_coord[0]) * px) ** 2 +
((y_centerline - coord_nearest_coord[1]) * py) ** 2 +
((i - slice_nearest_coord) * pz) ** 2)
if distance >= threshold_distance: # threshold must be adjusted, default is 5 mm
slices_to_remove[i] = True
# Check list of removal and keep one continuous centerline (improve this comment)
# Method:
# starting from mid-centerline (in both directions), the first True encountered is applied to all following slices
slice_to_change = False
for i in range(nz / 2, nz):
if slice_to_change:
slices_to_remove[i] = True
elif slices_to_remove[i]:
slices_to_remove[i] = True
slice_to_change = True
slice_to_change = False
for i in range(nz / 2, -1, -1):
if slice_to_change:
slices_to_remove[i] = True
elif slices_to_remove[i]:
slices_to_remove[i] = True
slice_to_change = True
for i in range(nz):
# remove the slice
if slices_to_remove[i]:
im_seg.data[:, :, i] *= 0
# saving the image
im_seg.setFileName('tmp.segmentation_RPI_c.nii.gz')
im_seg.save()
# replacing old segmentation with the corrected one
sct.run('sct_image -i tmp.segmentation_RPI_c.nii.gz -setorient ' + image_input_orientation + ' -o ../' + fname_segmentation, verbose)
os.chdir('..')
# display information about how much of the segmentation has been corrected
# remove temporary files
if remove_temp_files:
sct.printv("\nRemove temporary files...", verbose)
shutil.rmtree(path_tmp, ignore_errors=True)
if "-alpha" in arguments:
cmd += " -alpha " + str(arguments["-alpha"])
# check if input image is in 3D. Otherwise itk image reader will cut the 4D image in 3D volumes and only take the first one.
from msct_image import Image
image_input = Image(input_filename)
nx, ny, nz, nt, px, py, pz, pt = image_input.dim
if nt > 1:
sct.printv('ERROR: your input image needs to be 3D in order to be segmented.', 1, 'error')
path_fname, file_fname, ext_fname = sct.extract_fname(input_filename)
# if centerline or mask is asked using viewer
if use_viewer:
# make sure image is in SAL orientation, as it is the orientation used by PropSeg
image_input_orientation = orientation(image_input, get=True, verbose=False)
reoriented_image_filename = 'tmp.' + sct.add_suffix(file_fname + ext_fname, "_SAL")
path_tmp_viewer = sct.tmp_create(verbose=verbose)
sct.run('sct_image -i ' + input_filename + ' -o ' + path_tmp_viewer + reoriented_image_filename + ' -setorient SAL -v 0', verbose=False)
from sct_viewer import ClickViewer
image_input_reoriented = Image(path_tmp_viewer + reoriented_image_filename)
viewer = ClickViewer(image_input_reoriented)
viewer.help_url = 'https://sourceforge.net/p/spinalcordtoolbox/wiki/correction_PropSeg/attachment/propseg_viewer.png'
if use_viewer == "mask":
viewer.number_of_slices = 3
viewer.gap_inter_slice = int(10 / pz)
if viewer.gap_inter_slice == 0:
viewer.gap_inter_slice = 1
viewer.calculate_list_slices()
#else:
