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 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)
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")
sct_image.main(
args=['-i', image_fname, '-type', 'int16', '-o', image_int_filename])
# 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 = '../' + folder_output
sct.printv('Copy output to ' + folder_output, verbose=0)
shutil.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
shutil.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)
def set_orient(ref, i, i_tmp, o):
sct_image.main(args=['-i', ref, '-copy-header', i, '-v', '0'])
sct_image.main(
args=['-i', i, '-setorient-data', 'IPR', '-o', i_tmp, '-v', '0'])
sct_image.main(args=['-i', i_tmp, '-setorient', 'RPI', '-o', o, '-v', '0'])
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"), verbose=0)
convert(fname_centerline, os.path.join(path_tmp, "tmp.centerline.nii.gz"), 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 = im_input.orientation
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.save('tmp.segmentation_RPI_c.nii.gz')
# 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)
