def dmri_ail_cropped(tmp_path):
"""Reorient image to sagittal for testing another orientation (and crop to save time)."""
path_out = str(tmp_path / 'dmri_AIL_crop.nii')
sct_image.main(argv=['-i', 'dmri/dmri.nii.gz', '-setorient', 'AIL', '-o', 'dmri/dmri_AIL.nii'])
sct_crop_image.main(argv=['-i', 'dmri/dmri_AIL.nii', '-zmin', '19', '-zmax', '21', '-o', path_out])
return path_out
def test_sct_image_concat_dmri(tmp_path, dmri_t_slices, dmri_in):
"""Run the CLI script and verify concatenated image matches reference image."""
path_out = str(tmp_path / 'dmri_concat.nii.gz')
sct_image.main(argv=['-i'] + dmri_t_slices +
['-concat', 't', '-o', path_out])
ref = Image(dmri_in)
new = Image(path_out)
assert np.linalg.norm(ref.data - new.data) == 0
def dmri_t_slices(tmp_path, dmri_in):
"""Filepaths for 4D dMRI image split across t axis."""
fname_out = str(tmp_path / 'dmri.nii.gz')
sct_image.main(argv=['-i', dmri_in, '-split', 't', '-o', fname_out])
parent, filename, ext = extract_fname(fname_out)
fname_slices = [
os.path.join(parent, filename + '_T' + str(i).zfill(4) + ext)
for i in range(7)
]
return fname_slices
def test_sct_image_pad():
"""Run the CLI script and test the '-pad' option."""
pad = 2
path_in = 'mt/mtr.nii.gz' # 3D
path_out = 'sct_image_out.nii.gz'
sct_image.main(argv=[
'-i', path_in, '-o', 'sct_image_out.nii.gz', '-pad', f'0,0,{pad}'
])
# z axis (dim[2]) should be padded, but all other values should be unchanged
expected_dim = Image(path_in).dim[:2] + (
Image(path_in).dim[2] + (2 * pad), ) + Image(path_in).dim[3:]
assert Image(path_out).dim == expected_dim
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
"""
printv('\nCheck consistency of segmentation...', verbose)
# creating a temporary folder in which all temporary files will be placed and deleted afterwards
path_tmp = tmp_create(basename="propseg")
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
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:
# printv("\nRemove temporary files...", verbose)
rmtree(path_tmp)
def test_sct_image_display_warp_check_output_exists():
"""Run the CLI script and check that the warp image file was created."""
fname_in = 'warp_template2anat.nii.gz'
fname_out = 'grid_3_resample_' + fname_in
sct_image.main(argv=['-i', sct_test_path('t2', fname_in), '-display-warp'])
assert os.path.exists(sct_test_path('t2', fname_out))
def test_sct_image_show_header_no_checks(output_format):
"""Run the CLI script without checking results. The rationale for not checking results is
provided here: https://github.com/spinalcordtoolbox/spinalcordtoolbox/pull/3317#issuecomment-811429547"""
sct_image.main(argv=[
'-i', sct_test_path('t2', 't2.nii.gz'), '-header', output_format
])
def test_sct_image_getorient(path_in):
"""Run the CLI script and ."""
sct_image.main(argv=['-i', path_in, '-getorient'])