def dmri_mask(tmp_path):
"""Mask image for testing."""
path_out = str(tmp_path / 'mask.nii')
sct_create_mask.main(argv=['-i', 'dmri/dmri_T0000.nii.gz', '-p', 'center', '-size', '5mm', '-f', 'gaussian',
'-o', path_out])
return path_out
def test_sct_register_multimodal_with_softmask(tmp_path):
"""
Verify that softmask is actually applied during registration.
NB: For 'gaussian', ANTs binaries can't handle softmasks natively, so SCT should be applying
the mask directly to the image. Related links:
* https://github.com/ivadomed/pipeline-hemis/issues/3.
* https://github.com/spinalcordtoolbox/spinalcordtoolbox/issues/3075
"""
fname_mask = str(tmp_path / 'mask_t2.nii.gz')
fname_t2 = sct_test_path('t2', 't2.nii.gz')
fname_t1 = sct_test_path('t1', 't1w.nii.gz')
fname_warp = str(tmp_path / "warp_t1w2t2.nii.gz")
sct_create_mask.main([
'-i', fname_t2, '-p',
f"centerline,{sct_test_path('t2', 't2_centerline-manual.nii.gz')}",
'-o', fname_mask, '-f', 'gaussian'
])
sct_register_multimodal.main([
'-i', fname_t1, '-d', fname_t2, '-dseg',
sct_test_path('t2', 't2_seg-manual.nii.gz'), '-param',
"step=1,type=im,algo=slicereg,metric=CC", '-m', fname_mask, '-ofolder',
str(tmp_path), '-r', '0', '-v', '2'
])
# If registration was successful, the warping field should be non-empty
assert np.any(Image(fname_warp).data)
def test_sct_register_multimodal_mask_files_exist(tmp_path):
"""
Run the script without validating results.
- TODO: Write a check that verifies the registration results.
- TODO: Parametrize this test to add '-initwarpinv warp_anat2template.nii.gz',
after the file is added to sct_testing_data:
https://github.com/spinalcordtoolbox/spinalcordtoolbox/pull/3407#discussion_r646895013
"""
fname_mask = str(tmp_path / 'mask_mt1.nii.gz')
sct_create_mask.main([
'-i',
sct_test_path('mt', 'mt1.nii.gz'), '-p',
f"centerline,{sct_test_path('mt', 'mt1_seg.nii.gz')}", '-size', '35mm',
'-f', 'cylinder', '-o', fname_mask
])
sct_register_multimodal.main([
'-i',
sct_dir_local_path('data/PAM50/template/', 'PAM50_t2.nii.gz'), '-iseg',
sct_dir_local_path('data/PAM50/template/', 'PAM50_cord.nii.gz'), '-d',
sct_test_path('mt', 'mt1.nii.gz'), '-dseg',
sct_test_path('mt', 'mt1_seg.nii.gz'), '-param',
'step=1,type=seg,algo=centermass:step=2,type=seg,algo=bsplinesyn,slicewise=1,iter=3',
'-m', fname_mask, '-initwarp',
sct_test_path('t2', 'warp_template2anat.nii.gz'), '-ofolder',
str(tmp_path)
])
for path in ["PAM50_t2_reg.nii.gz", "warp_PAM50_t22mt1.nii.gz"]:
assert os.path.exists(tmp_path / path)
# Because `-initwarp` was specified (but `-initwarpinv` wasn't) the dest->seg files should NOT exist
for path in ["mt1_reg.nii.gz", "warp_mt12PAM50_t2.nii.gz"]:
assert not os.path.exists(tmp_path / path)
def test_sct_register_multimodal_mask_no_checks(tmp_path):
"""Run the script without validating results.
TODO: Write a check that verifies the registration results as part of
https://github.com/spinalcordtoolbox/spinalcordtoolbox/pull/3246."""
fname_mask = str(tmp_path / 'mask_mt1.nii.gz')
sct_create_mask.main([
'-i',
sct_test_path('mt', 'mt1.nii.gz'), '-p',
f"centerline,{sct_test_path('mt', 'mt1_seg.nii.gz')}", '-size', '35mm',
'-f', 'cylinder', '-o', fname_mask
])
sct_register_multimodal.main([
'-i',
sct_dir_local_path('data/PAM50/template/', 'PAM50_t2.nii.gz'), '-iseg',
sct_dir_local_path('data/PAM50/template/', 'PAM50_cord.nii.gz'), '-d',
sct_test_path('mt', 'mt1.nii.gz'), '-dseg',
sct_test_path('mt', 'mt1_seg.nii.gz'), '-param',
'step=1,type=seg,algo=centermass:step=2,type=seg,algo=bsplinesyn,slicewise=1,iter=3',
'-m', fname_mask, '-initwarp',
sct_test_path('t2', 'warp_template2anat.nii.gz')
])
def test_sct_create_mask_no_checks(path_input, process, size):
"""Run the CLI script without checking results.
TODO: Check the results. (This test replaces the 'sct_testing' test, which did not implement any checks.)"""
sct_create_mask.main(argv=['-i', path_input, '-p', process, '-size', size, '-r', '0'])
def pre_processing(fname_target,
fname_sc_seg,
fname_level=None,
fname_manual_gmseg=None,
new_res=0.3,
square_size_size_mm=22.5,
denoising=True,
verbose=1,
rm_tmp=True,
for_model=False):
printv('\nPre-process data...', verbose, 'normal')
tmp_dir = tmp_create()
copy(fname_target, tmp_dir)
fname_target = ''.join(extract_fname(fname_target)[1:])
copy(fname_sc_seg, tmp_dir)
fname_sc_seg = ''.join(extract_fname(fname_sc_seg)[1:])
curdir = os.getcwd()
os.chdir(tmp_dir)
original_info = {
'orientation': None,
'im_sc_seg_rpi': None,
'interpolated_images': []
}
im_target = Image(fname_target).copy()
im_sc_seg = Image(fname_sc_seg).copy()
# get original orientation
printv(' Reorient...', verbose, 'normal')
original_info['orientation'] = im_target.orientation
# assert images are in the same orientation
assert im_target.orientation == im_sc_seg.orientation, "ERROR: the image to segment and it's SC segmentation are not in the same orientation"
im_target_rpi = im_target.copy().change_orientation(
'RPI', generate_path=True).save()
im_sc_seg_rpi = im_sc_seg.copy().change_orientation(
'RPI', generate_path=True).save()
original_info['im_sc_seg_rpi'] = im_sc_seg_rpi.copy(
) # target image in RPI will be used to post-process segmentations
# denoise using P. Coupe non local means algorithm (see [Manjon et al. JMRI 2010]) implemented in dipy
if denoising:
printv(' Denoise...', verbose, 'normal')
# crop image before denoising to fasten denoising
nx, ny, nz, nt, px, py, pz, pt = im_target_rpi.dim
size_x, size_y = (square_size_size_mm + 1) / px, (square_size_size_mm +
1) / py
size = int(np.ceil(max(size_x, size_y)))
# create mask
fname_mask = 'mask_pre_crop.nii.gz'
sct_create_mask.main([
'-i', im_target_rpi.absolutepath, '-p',
'centerline,' + im_sc_seg_rpi.absolutepath, '-f', 'box', '-size',
str(size), '-o', fname_mask
])
# crop image
cropper = ImageCropper(im_target_rpi)
cropper.get_bbox_from_mask(Image(fname_mask))
im_target_rpi_crop = cropper.crop()
# crop segmentation
cropper = ImageCropper(im_sc_seg_rpi)
cropper.get_bbox_from_mask(Image(fname_mask))
im_sc_seg_rpi_crop = cropper.crop()
# denoising
from spinalcordtoolbox.math import denoise_nlmeans
block_radius = 3
block_radius = int(
im_target_rpi_crop.data.shape[2] /
2) if im_target_rpi_crop.data.shape[2] < (block_radius *
2) else block_radius
patch_radius = block_radius - 1
data_denoised = denoise_nlmeans(im_target_rpi_crop.data,
block_radius=block_radius,
patch_radius=patch_radius)
im_target_rpi_crop.data = data_denoised
im_target_rpi = im_target_rpi_crop
im_sc_seg_rpi = im_sc_seg_rpi_crop
else:
fname_mask = None
# interpolate image to reference square image (resample and square crop centered on SC)
printv(' Interpolate data to the model space...', verbose, 'normal')
list_im_slices = interpolate_im_to_ref(im_target_rpi,
im_sc_seg_rpi,
new_res=new_res,
sq_size_size_mm=square_size_size_mm)
original_info[
'interpolated_images'] = list_im_slices # list of images (not Slice() objects)
printv(' Mask data using the spinal cord segmentation...', verbose,
'normal')
list_sc_seg_slices = interpolate_im_to_ref(
im_sc_seg_rpi,
im_sc_seg_rpi,
new_res=new_res,
sq_size_size_mm=square_size_size_mm,
interpolation_mode=1)
for i in range(len(list_im_slices)):
# list_im_slices[i].data[list_sc_seg_slices[i].data == 0] = 0
list_sc_seg_slices[i] = binarize(list_sc_seg_slices[i],
thr_min=0.5,
thr_max=1)
list_im_slices[
i].data = list_im_slices[i].data * list_sc_seg_slices[i].data
printv(' Split along rostro-caudal direction...', verbose, 'normal')
list_slices_target = [
Slice(slice_id=i, im=im_slice.data, gm_seg=[], wm_seg=[])
for i, im_slice in enumerate(list_im_slices)
]
# load vertebral levels
if fname_level is not None:
printv(' Load vertebral levels...', verbose, 'normal')
# copy level file to tmp dir
os.chdir(curdir)
copy(fname_level, tmp_dir)
os.chdir(tmp_dir)
# change fname level to only file name (path = tmp dir now)
fname_level = ''.join(extract_fname(fname_level)[1:])
# load levels
list_slices_target = load_level(list_slices_target, fname_level)
os.chdir(curdir)
# load manual gmseg if there is one (model data)
if fname_manual_gmseg is not None:
printv('\n\tLoad manual GM segmentation(s) ...', verbose, 'normal')
list_slices_target = load_manual_gmseg(list_slices_target,
fname_manual_gmseg,
tmp_dir,
im_sc_seg_rpi,
new_res,
square_size_size_mm,
for_model=for_model,
fname_mask=fname_mask)
if rm_tmp:
# remove tmp folder
rmtree(tmp_dir)
return list_slices_target, original_info