def crop_from_mask_with_background(self):
image_in = Image(self.input_filename)
data_array = np.asarray(image_in.data)
data_mask = np.asarray(Image(self.mask).data)
assert data_array.shape == data_mask.shape
# Element-wise matrix multiplication:
new_data = None
dim = len(data_array.shape)
if dim == 3:
new_data = data_mask * data_array
elif dim == 2:
new_data = data_mask * data_array
if self.background != 0:
from sct_maths import get_data_or_scalar
data_background = get_data_or_scalar(str(self.background), data_array)
data_mask_inv = data_mask.max() - data_mask
if dim == 3:
data_background = data_mask_inv * data_background
elif dim == 2:
data_background = data_mask_inv * data_background
new_data += data_background
image_out = msct_image.empty_like(image_in)
image_out.data = new_data
image_out.save(self.output_filename)
def crop_image_around_centerline(im_in, ctr_in, crop_size):
"""Crop the input image around the input centerline file."""
data_ctr = ctr_in.data
data_ctr = data_ctr if len(data_ctr.shape) >= 3 else np.expand_dims(data_ctr, 2)
data_in = im_in.data.astype(np.float32)
im_new = empty_like(im_in) # but in fact we're going to crop it
x_lst, y_lst, z_lst = [], [], []
data_im_new = np.zeros((crop_size, crop_size, im_in.dim[2]))
for zz in range(im_in.dim[2]):
if np.any(np.array(data_ctr[:, :, zz])):
x_ctr, y_ctr = center_of_mass(np.array(data_ctr[:, :, zz]))
x_start, x_end = _find_crop_start_end(x_ctr, crop_size, im_in.dim[0])
y_start, y_end = _find_crop_start_end(y_ctr, crop_size, im_in.dim[1])
crop_im = np.zeros((crop_size, crop_size))
x_shape, y_shape = data_in[x_start:x_end, y_start:y_end, zz].shape
crop_im[:x_shape, :y_shape] = data_in[x_start:x_end, y_start:y_end, zz]
data_im_new[:, :, zz] = crop_im
x_lst.append(str(x_start))
y_lst.append(str(y_start))
z_lst.append(zz)
im_new.data = data_im_new
return x_lst, y_lst, z_lst, im_new
def crop_from_mask_with_background(self):
image_in = Image(self.input_filename)
data_array = np.asarray(image_in.data)
data_mask = np.asarray(Image(self.mask).data)
assert data_array.shape == data_mask.shape
# Element-wise matrix multiplication:
new_data = None
dim = len(data_array.shape)
if dim == 3:
new_data = data_mask * data_array
elif dim == 2:
new_data = data_mask * data_array
if self.background != 0:
from sct_maths import get_data_or_scalar
data_background = get_data_or_scalar(str(self.background),
data_array)
data_mask_inv = data_mask.max() - data_mask
if dim == 3:
data_background = data_mask_inv * data_background
elif dim == 2:
data_background = data_mask_inv * data_background
new_data += data_background
image_out = msct_image.empty_like(image_in)
image_out.data = new_data
image_out.save(self.output_filename)
def crop_image_around_centerline(im_in, ctr_in, crop_size):
"""Crop the input image around the input centerline file."""
data_ctr = ctr_in.data
data_ctr = data_ctr if len(data_ctr.shape) >= 3 else np.expand_dims(
data_ctr, 2)
data_in = im_in.data.astype(np.float32)
im_new = empty_like(im_in) # but in fact we're going to crop it
x_lst, y_lst, z_lst = [], [], []
data_im_new = np.zeros((crop_size, crop_size, im_in.dim[2]))
for zz in range(im_in.dim[2]):
if np.any(np.array(data_ctr[:, :, zz])):
x_ctr, y_ctr = center_of_mass(np.array(data_ctr[:, :, zz]))
x_start, x_end = _find_crop_start_end(x_ctr, crop_size,
im_in.dim[0])
y_start, y_end = _find_crop_start_end(y_ctr, crop_size,
im_in.dim[1])
crop_im = np.zeros((crop_size, crop_size))
x_shape, y_shape = data_in[x_start:x_end, y_start:y_end, zz].shape
crop_im[:x_shape, :y_shape] = data_in[x_start:x_end, y_start:y_end,
zz]
data_im_new[:, :, zz] = crop_im
x_lst.append(str(x_start))
y_lst.append(str(y_start))
z_lst.append(zz)
im_new.data = data_im_new
return x_lst, y_lst, z_lst, im_new
def apply_intensity_normalization(img, contrast):
"""Standardize the intensity range."""
data2norm = img.data.astype(np.float32)
dct_norm = {
't2': [
0.000000, 136.832187, 312.158435, 448.968030, 568.657779,
696.671586, 859.221138, 1074.463414, 1373.289174, 1811.522669,
2611.000000
],
't2_ax': [
0.000000, 112.195357, 291.611185, 446.727066, 581.103970,
702.979079, 833.318257, 1011.856313, 1268.801813, 1687.137075,
2611.000000
],
't2s': [
0.000000, 123.246969, 226.422561, 338.361023, 532.341924,
788.693675, 1096.975553, 1407.979466, 1716.524530, 2079.788451,
2611.000000
]
}
img_normalized = msct_image.empty_like(img)
img_normalized.data = apply_intensity_normalization_model(
data2norm, dct_norm[contrast])
return img_normalized
def apply_intensity_normalization(img, contrast):
"""Standardize the intensity range."""
data2norm = img.data.astype(np.float32)
dct_norm = {'t2': [0.000000, 136.832187, 312.158435, 448.968030, 568.657779, 696.671586, 859.221138, 1074.463414, 1373.289174, 1811.522669, 2611.000000],
't2_ax': [0.000000, 112.195357, 291.611185, 446.727066, 581.103970, 702.979079, 833.318257, 1011.856313, 1268.801813, 1687.137075, 2611.000000],
't2s': [0.000000, 123.246969, 226.422561, 338.361023, 532.341924, 788.693675, 1096.975553, 1407.979466, 1716.524530, 2079.788451, 2611.000000]}
img_normalized = msct_image.empty_like(img)
img_normalized.data = apply_intensity_normalization_model(data2norm, dct_norm[contrast])
return img_normalized
def __init__(self, im, v=1):
sct.printv('Thinning ... ', v, 'normal')
self.image = im
self.image.data = bin_data(self.image.data)
self.dim_im = len(self.image.data.shape)
if self.dim_im == 2:
self.thinned_image = msct_image.empty_like(self.image)
self.thinned_image.data = self.zhang_suen(self.image.data)
self.thinned_image.absolutepath = sct.add_suffix(self.image.absolutepath, "_thinned")
elif self.dim_im == 3:
if not self.image.orientation == 'IRP':
sct.printv('-- changing orientation ...')
self.image.change_orientation('IRP')
thinned_data = np.asarray([self.zhang_suen(im_slice) for im_slice in self.image.data])
self.thinned_image = msct_image.empty_like(self.image)
self.thinned_image.data = thinned_data
self.thinned_image.absolutepath = sct.add_suffix(self.image.absolutepath, "_thinned")
def __init__(self, im, v=1):
sct.printv('Thinning ... ', v, 'normal')
self.image = im
self.image.data = bin_data(self.image.data)
self.dim_im = len(self.image.data.shape)
if self.dim_im == 2:
self.thinned_image = msct_image.empty_like(self.image)
self.thinned_image.data = self.zhang_suen(self.image.data)
self.thinned_image.absolutepath = sct.add_suffix(self.image.absolutepath, "_thinned")
elif self.dim_im == 3:
if not self.image.orientation == 'IRP':
sct.printv('-- changing orientation ...')
self.image.change_orientation('IRP')
thinned_data = np.asarray([self.zhang_suen(im_slice) for im_slice in self.image.data])
self.thinned_image = msct_image.empty_like(self.image)
self.thinned_image.data = thinned_data
self.thinned_image.absolutepath = sct.add_suffix(self.image.absolutepath, "_thinned")
def split_data(im_in, dim, squeeze_data=True):
"""
Split data
:param im_in: input image.
:param dim: dimension: 0, 1, 2, 3.
:return: list of split images
"""
dim_list = ['x', 'y', 'z', 't']
# Parse file name
# Open first file.
data = im_in.data
# in case input volume is 3d and dim=t, create new axis
if dim + 1 > len(np.shape(data)):
data = data[..., np.newaxis]
# in case splitting along the last dim, make sure to remove the last dim to avoid singleton
if dim + 1 == len(np.shape(data)):
if squeeze_data:
do_reshape = True
else:
do_reshape = False
else:
do_reshape = False
# Split data into list
data_split = np.array_split(data, data.shape[dim], dim)
# Write each file
im_out_list = []
for idx_img, dat in enumerate(data_split):
im_out = msct_image.empty_like(im_in)
if do_reshape:
im_out.data = dat.reshape(
tuple([
x for (idx_shape, x) in enumerate(data.shape)
if idx_shape != dim
]))
else:
im_out.data = dat
im_out.absolutepath = sct.add_suffix(
im_in.absolutepath, "_{}{}".format(dim_list[dim].upper(),
str(idx_img).zfill(4)))
im_out_list.append(im_out)
return im_out_list
def compute(self):
fname_data = self.fmri
# open data
nii_data = Image(fname_data)
data = nii_data.data
# compute mean
data_mean = np.mean(data, 3)
# compute STD
data_std = np.std(data, 3, ddof=1)
# compute TSNR
data_tsnr = data_mean / data_std
# save TSNR
fname_tsnr = self.out
nii_tsnr = msct_image.empty_like(nii_data)
nii_tsnr.data = data_tsnr
nii_tsnr.save(fname_tsnr, dtype=np.float32)
sct.display_viewer_syntax([fname_tsnr])
def split_data(im_in, dim, squeeze_data=True):
"""
Split data
:param im_in: input image.
:param dim: dimension: 0, 1, 2, 3.
:return: list of split images
"""
dim_list = ['x', 'y', 'z', 't']
# Parse file name
# Open first file.
data = im_in.data
# in case input volume is 3d and dim=t, create new axis
if dim + 1 > len(np.shape(data)):
data = data[..., np.newaxis]
# in case splitting along the last dim, make sure to remove the last dim to avoid singleton
if dim + 1 == len(np.shape(data)):
if squeeze_data:
do_reshape = True
else:
do_reshape = False
else:
do_reshape = False
# Split data into list
data_split = np.array_split(data, data.shape[dim], dim)
# Write each file
im_out_list = []
for idx_img, dat in enumerate(data_split):
im_out = msct_image.empty_like(im_in)
if do_reshape:
im_out.data = dat.reshape(tuple([ x for (idx_shape, x) in enumerate(data.shape) if idx_shape != dim]))
else:
im_out.data = dat
im_out.absolutepath = sct.add_suffix(im_in.absolutepath, "_{}{}".format(dim_list[dim].upper(), str(idx_img).zfill(4)))
im_out_list.append(im_out)
return im_out_list
def create_mask(param):
# parse argument for method
method_type = param.process[0]
# check method val
if not method_type == 'center':
method_val = param.process[1]
# check existence of input files
if method_type == 'centerline':
check_file_exist(method_val, param.verbose)
# Extract path/file/extension
path_data, file_data, ext_data = extract_fname(param.fname_data)
# Get output folder and file name
if param.fname_out == '':
param.fname_out = os.path.abspath(param.file_prefix + file_data +
ext_data)
path_tmp = tmp_create(basename="create_mask")
printv('\nOrientation:', param.verbose)
orientation_input = Image(param.fname_data).orientation
printv(' ' + orientation_input, param.verbose)
# copy input data to tmp folder and re-orient to RPI
Image(param.fname_data).change_orientation("RPI").save(
os.path.join(path_tmp, "data_RPI.nii"))
if method_type == 'centerline':
Image(method_val).change_orientation("RPI").save(
os.path.join(path_tmp, "centerline_RPI.nii"))
if method_type == 'point':
Image(method_val).change_orientation("RPI").save(
os.path.join(path_tmp, "point_RPI.nii"))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Get dimensions of data
im_data = Image('data_RPI.nii')
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
printv('\nDimensions:', param.verbose)
printv(im_data.dim, param.verbose)
# in case user input 4d data
if nt != 1:
printv(
'WARNING in ' + os.path.basename(__file__) +
': Input image is 4d but output mask will be 3D from first time slice.',
param.verbose, 'warning')
# extract first volume to have 3d reference
nii = empty_like(Image('data_RPI.nii'))
data3d = nii.data[:, :, :, 0]
nii.data = data3d
nii.save('data_RPI.nii')
if method_type == 'coord':
# parse to get coordinate
coord = [x for x in map(int, method_val.split('x'))]
if method_type == 'point':
# extract coordinate of point
printv('\nExtract coordinate of point...', param.verbose)
coord = Image("point_RPI.nii").getNonZeroCoordinates()
if method_type == 'center':
# set coordinate at center of FOV
coord = np.round(float(nx) / 2), np.round(float(ny) / 2)
if method_type == 'centerline':
# get name of centerline from user argument
fname_centerline = 'centerline_RPI.nii'
else:
# generate volume with line along Z at coordinates 'coord'
printv('\nCreate line...', param.verbose)
fname_centerline = create_line(param, 'data_RPI.nii', coord, nz)
# create mask
printv('\nCreate mask...', param.verbose)
centerline = nibabel.load(fname_centerline) # open centerline
hdr = centerline.get_header() # get header
hdr.set_data_dtype('uint8') # set imagetype to uint8
spacing = hdr.structarr['pixdim']
data_centerline = centerline.get_data() # get centerline
# if data is 2D, reshape with empty third dimension
if len(data_centerline.shape) == 2:
data_centerline_shape = list(data_centerline.shape)
data_centerline_shape.append(1)
data_centerline = data_centerline.reshape(data_centerline_shape)
z_centerline_not_null = [
iz for iz in range(0, nz, 1) if data_centerline[:, :, iz].any()
]
# get center of mass of the centerline
cx = [0] * nz
cy = [0] * nz
for iz in range(0, nz, 1):
if iz in z_centerline_not_null:
cx[iz], cy[iz] = ndimage.measurements.center_of_mass(
np.array(data_centerline[:, :, iz]))
# create 2d masks
im_list = []
for iz in range(nz):
if iz not in z_centerline_not_null:
im_list.append(Image(data_centerline[:, :, iz], hdr=hdr))
else:
center = np.array([cx[iz], cy[iz]])
mask2d = create_mask2d(param,
center,
param.shape,
param.size,
im_data=im_data)
im_list.append(Image(mask2d, hdr=hdr))
im_out = concat_data(im_list, dim=2).save('mask_RPI.nii.gz')
im_out.change_orientation(orientation_input)
im_out.header = Image(param.fname_data).header
im_out.save(param.fname_out)
# come back
os.chdir(curdir)
# Remove temporary files
if param.remove_temp_files == 1:
printv('\nRemove temporary files...', param.verbose)
rmtree(path_tmp)
display_viewer_syntax([param.fname_data, param.fname_out],
colormaps=['gray', 'red'],
opacities=['', '0.5'])
def main(args=None):
# initializations
param = Param()
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(args)
fname_data = arguments['-i']
fname_seg = arguments['-s']
if '-l' in arguments:
fname_landmarks = arguments['-l']
label_type = 'body'
elif '-ldisc' in arguments:
fname_landmarks = arguments['-ldisc']
label_type = 'disc'
else:
sct.printv('ERROR: Labels should be provided.', 1, 'error')
if '-ofolder' in arguments:
path_output = arguments['-ofolder']
else:
path_output = ''
param.path_qc = arguments.get("-qc", None)
path_template = arguments['-t']
contrast_template = arguments['-c']
ref = arguments['-ref']
param.remove_temp_files = int(arguments.get('-r'))
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
param.verbose = verbose # TODO: not clean, unify verbose or param.verbose in code, but not both
param_centerline = ParamCenterline(
algo_fitting=arguments['-centerline-algo'],
smooth=arguments['-centerline-smooth'])
# registration parameters
if '-param' in arguments:
# reset parameters but keep step=0 (might be overwritten if user specified step=0)
paramreg = ParamregMultiStep([step0])
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# add user parameters
for paramStep in arguments['-param']:
paramreg.addStep(paramStep)
else:
paramreg = ParamregMultiStep([step0, step1, step2])
# if ref=subject, initialize registration using different affine parameters
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# initialize other parameters
zsubsample = param.zsubsample
# retrieve template file names
file_template_vertebral_labeling = get_file_label(os.path.join(path_template, 'template'), 'vertebral labeling')
file_template = get_file_label(os.path.join(path_template, 'template'), contrast_template.upper() + '-weighted template')
file_template_seg = get_file_label(os.path.join(path_template, 'template'), 'spinal cord')
# start timer
start_time = time.time()
# get fname of the template + template objects
fname_template = os.path.join(path_template, 'template', file_template)
fname_template_vertebral_labeling = os.path.join(path_template, 'template', file_template_vertebral_labeling)
fname_template_seg = os.path.join(path_template, 'template', file_template_seg)
fname_template_disc_labeling = os.path.join(path_template, 'template', 'PAM50_label_disc.nii.gz')
# check file existence
# TODO: no need to do that!
sct.printv('\nCheck template files...')
sct.check_file_exist(fname_template, verbose)
sct.check_file_exist(fname_template_vertebral_labeling, verbose)
sct.check_file_exist(fname_template_seg, verbose)
path_data, file_data, ext_data = sct.extract_fname(fname_data)
# sct.printv(arguments)
sct.printv('\nCheck parameters:', verbose)
sct.printv(' Data: ' + fname_data, verbose)
sct.printv(' Landmarks: ' + fname_landmarks, verbose)
sct.printv(' Segmentation: ' + fname_seg, verbose)
sct.printv(' Path template: ' + path_template, verbose)
sct.printv(' Remove temp files: ' + str(param.remove_temp_files), verbose)
# check input labels
labels = check_labels(fname_landmarks, label_type=label_type)
vertebral_alignment = False
if len(labels) > 2 and label_type == 'disc':
vertebral_alignment = True
path_tmp = sct.tmp_create(basename="register_to_template", verbose=verbose)
# set temporary file names
ftmp_data = 'data.nii'
ftmp_seg = 'seg.nii.gz'
ftmp_label = 'label.nii.gz'
ftmp_template = 'template.nii'
ftmp_template_seg = 'template_seg.nii.gz'
ftmp_template_label = 'template_label.nii.gz'
# copy files to temporary folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
Image(fname_data).save(os.path.join(path_tmp, ftmp_data))
Image(fname_seg).save(os.path.join(path_tmp, ftmp_seg))
Image(fname_landmarks).save(os.path.join(path_tmp, ftmp_label))
Image(fname_template).save(os.path.join(path_tmp, ftmp_template))
Image(fname_template_seg).save(os.path.join(path_tmp, ftmp_template_seg))
Image(fname_template_vertebral_labeling).save(os.path.join(path_tmp, ftmp_template_label))
if label_type == 'disc':
Image(fname_template_disc_labeling).save(os.path.join(path_tmp, ftmp_template_label))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Generate labels from template vertebral labeling
if label_type == 'body':
sct.printv('\nGenerate labels from template vertebral labeling', verbose)
ftmp_template_label_, ftmp_template_label = ftmp_template_label, sct.add_suffix(ftmp_template_label, "_body")
sct_label_utils.main(args=['-i', ftmp_template_label_, '-vert-body', '0', '-o', ftmp_template_label])
# check if provided labels are available in the template
sct.printv('\nCheck if provided labels are available in the template', verbose)
image_label_template = Image(ftmp_template_label)
labels_template = image_label_template.getNonZeroCoordinates(sorting='value')
if labels[-1].value > labels_template[-1].value:
sct.printv('ERROR: Wrong landmarks input. Labels must have correspondence in template space. \nLabel max '
'provided: ' + str(labels[-1].value) + '\nLabel max from template: ' +
str(labels_template[-1].value), verbose, 'error')
# if only one label is present, force affine transformation to be Tx,Ty,Tz only (no scaling)
if len(labels) == 1:
paramreg.steps['0'].dof = 'Tx_Ty_Tz'
sct.printv('WARNING: Only one label is present. Forcing initial transformation to: ' + paramreg.steps['0'].dof,
1, 'warning')
# Project labels onto the spinal cord centerline because later, an affine transformation is estimated between the
# template's labels (centered in the cord) and the subject's labels (assumed to be centered in the cord).
# If labels are not centered, mis-registration errors are observed (see issue #1826)
ftmp_label = project_labels_on_spinalcord(ftmp_label, ftmp_seg, param_centerline)
# binarize segmentation (in case it has values below 0 caused by manual editing)
sct.printv('\nBinarize segmentation', verbose)
ftmp_seg_, ftmp_seg = ftmp_seg, sct.add_suffix(ftmp_seg, "_bin")
sct_maths.main(['-i', ftmp_seg_,
'-bin', '0.5',
'-o', ftmp_seg])
# Switch between modes: subject->template or template->subject
if ref == 'template':
# resample data to 1mm isotropic
sct.printv('\nResample data to 1mm isotropic...', verbose)
resample_file(ftmp_data, add_suffix(ftmp_data, '_1mm'), '1.0x1.0x1.0', 'mm', 'linear', verbose)
ftmp_data = add_suffix(ftmp_data, '_1mm')
resample_file(ftmp_seg, add_suffix(ftmp_seg, '_1mm'), '1.0x1.0x1.0', 'mm', 'linear', verbose)
ftmp_seg = add_suffix(ftmp_seg, '_1mm')
# N.B. resampling of labels is more complicated, because they are single-point labels, therefore resampling
# with nearest neighbour can make them disappear.
resample_labels(ftmp_label, ftmp_data, add_suffix(ftmp_label, '_1mm'))
ftmp_label = add_suffix(ftmp_label, '_1mm')
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
ftmp_data = Image(ftmp_data).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_seg = Image(ftmp_seg).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_label = Image(ftmp_label).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_crop')
if vertebral_alignment:
# cropping the segmentation based on the label coverage to ensure good registration with vertebral alignment
# See https://github.com/neuropoly/spinalcordtoolbox/pull/1669 for details
image_labels = Image(ftmp_label)
coordinates_labels = image_labels.getNonZeroCoordinates(sorting='z')
nx, ny, nz, nt, px, py, pz, pt = image_labels.dim
offset_crop = 10.0 * pz # cropping the image 10 mm above and below the highest and lowest label
cropping_slices = [coordinates_labels[0].z - offset_crop, coordinates_labels[-1].z + offset_crop]
# make sure that the cropping slices do not extend outside of the slice range (issue #1811)
if cropping_slices[0] < 0:
cropping_slices[0] = 0
if cropping_slices[1] > nz:
cropping_slices[1] = nz
msct_image.spatial_crop(Image(ftmp_seg_), dict(((2, np.int32(np.round(cropping_slices))),))).save(ftmp_seg)
else:
# if we do not align the vertebral levels, we crop the segmentation from top to bottom
im_seg_rpi = Image(ftmp_seg_)
bottom = 0
for data in msct_image.SlicerOneAxis(im_seg_rpi, "IS"):
if (data != 0).any():
break
bottom += 1
top = im_seg_rpi.data.shape[2]
for data in msct_image.SlicerOneAxis(im_seg_rpi, "SI"):
if (data != 0).any():
break
top -= 1
msct_image.spatial_crop(im_seg_rpi, dict(((2, (bottom, top)),))).save(ftmp_seg)
# straighten segmentation
sct.printv('\nStraighten the spinal cord using centerline/segmentation...', verbose)
# check if warp_curve2straight and warp_straight2curve already exist (i.e. no need to do it another time)
fn_warp_curve2straight = os.path.join(curdir, "warp_curve2straight.nii.gz")
fn_warp_straight2curve = os.path.join(curdir, "warp_straight2curve.nii.gz")
fn_straight_ref = os.path.join(curdir, "straight_ref.nii.gz")
cache_input_files=[ftmp_seg]
if vertebral_alignment:
cache_input_files += [
ftmp_template_seg,
ftmp_label,
ftmp_template_label,
]
cache_sig = sct.cache_signature(
input_files=cache_input_files,
)
cachefile = os.path.join(curdir, "straightening.cache")
if sct.cache_valid(cachefile, cache_sig) and os.path.isfile(fn_warp_curve2straight) and os.path.isfile(fn_warp_straight2curve) and os.path.isfile(fn_straight_ref):
sct.printv('Reusing existing warping field which seems to be valid', verbose, 'warning')
sct.copy(fn_warp_curve2straight, 'warp_curve2straight.nii.gz')
sct.copy(fn_warp_straight2curve, 'warp_straight2curve.nii.gz')
sct.copy(fn_straight_ref, 'straight_ref.nii.gz')
# apply straightening
sct_apply_transfo.main(args=[
'-i', ftmp_seg,
'-w', 'warp_curve2straight.nii.gz',
'-d', 'straight_ref.nii.gz',
'-o', add_suffix(ftmp_seg, '_straight')])
else:
from spinalcordtoolbox.straightening import SpinalCordStraightener
sc_straight = SpinalCordStraightener(ftmp_seg, ftmp_seg)
sc_straight.param_centerline = param_centerline
sc_straight.output_filename = add_suffix(ftmp_seg, '_straight')
sc_straight.path_output = './'
sc_straight.qc = '0'
sc_straight.remove_temp_files = param.remove_temp_files
sc_straight.verbose = verbose
if vertebral_alignment:
sc_straight.centerline_reference_filename = ftmp_template_seg
sc_straight.use_straight_reference = True
sc_straight.discs_input_filename = ftmp_label
sc_straight.discs_ref_filename = ftmp_template_label
sc_straight.straighten()
sct.cache_save(cachefile, cache_sig)
# N.B. DO NOT UPDATE VARIABLE ftmp_seg BECAUSE TEMPORARY USED LATER
# re-define warping field using non-cropped space (to avoid issue #367)
sct_concat_transfo.main(args=[
'-w', 'warp_straight2curve.nii.gz',
'-d', ftmp_data,
'-o', 'warp_straight2curve.nii.gz'])
if vertebral_alignment:
sct.copy('warp_curve2straight.nii.gz', 'warp_curve2straightAffine.nii.gz')
else:
# Label preparation:
# --------------------------------------------------------------------------------
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run(['sct_label_utils', '-i', ftmp_template_label, '-o', ftmp_template_label, '-remove-reference', ftmp_label])
# Dilating the input label so they can be straighten without losing them
sct.printv('\nDilating input labels using 3vox ball radius')
sct_maths.main(['-i', ftmp_label,
'-dilate', '3',
'-o', add_suffix(ftmp_label, '_dilate')])
ftmp_label = add_suffix(ftmp_label, '_dilate')
# Apply straightening to labels
sct.printv('\nApply straightening to labels...', verbose)
sct_apply_transfo.main(args=[
'-i', ftmp_label,
'-o', add_suffix(ftmp_label, '_straight'),
'-d', add_suffix(ftmp_seg, '_straight'),
'-w', 'warp_curve2straight.nii.gz',
'-x', 'nn'])
ftmp_label = add_suffix(ftmp_label, '_straight')
# Compute rigid transformation straight landmarks --> template landmarks
sct.printv('\nEstimate transformation for step #0...', verbose)
try:
register_landmarks(ftmp_label, ftmp_template_label, paramreg.steps['0'].dof,
fname_affine='straight2templateAffine.txt', verbose=verbose)
except RuntimeError:
raise('Input labels do not seem to be at the right place. Please check the position of the labels. '
'See documentation for more details: https://www.slideshare.net/neuropoly/sct-course-20190121/42')
# Concatenate transformations: curve --> straight --> affine
sct.printv('\nConcatenate transformations: curve --> straight --> affine...', verbose)
sct_concat_transfo.main(args=[
'-w', ['warp_curve2straight.nii.gz', 'straight2templateAffine.txt'],
'-d', 'template.nii',
'-o', 'warp_curve2straightAffine.nii.gz'])
# Apply transformation
sct.printv('\nApply transformation...', verbose)
sct_apply_transfo.main(args=[
'-i', ftmp_data,
'-o', add_suffix(ftmp_data, '_straightAffine'),
'-d', ftmp_template,
'-w', 'warp_curve2straightAffine.nii.gz'])
ftmp_data = add_suffix(ftmp_data, '_straightAffine')
sct_apply_transfo.main(args=[
'-i', ftmp_seg,
'-o', add_suffix(ftmp_seg, '_straightAffine'),
'-d', ftmp_template,
'-w', 'warp_curve2straightAffine.nii.gz',
'-x', 'linear'])
ftmp_seg = add_suffix(ftmp_seg, '_straightAffine')
"""
# Benjamin: Issue from Allan Martin, about the z=0 slice that is screwed up, caused by the affine transform.
# Solution found: remove slices below and above landmarks to avoid rotation effects
points_straight = []
for coord in landmark_template:
points_straight.append(coord.z)
min_point, max_point = int(np.round(np.min(points_straight))), int(np.round(np.max(points_straight)))
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_black')
msct_image.spatial_crop(Image(ftmp_seg_), dict(((2, (min_point,max_point)),))).save(ftmp_seg)
"""
# open segmentation
im = Image(ftmp_seg)
im_new = msct_image.empty_like(im)
# binarize
im_new.data = im.data > 0.5
# find min-max of anat2template (for subsequent cropping)
zmin_template, zmax_template = msct_image.find_zmin_zmax(im_new, threshold=0.5)
# save binarized segmentation
im_new.save(add_suffix(ftmp_seg, '_bin')) # unused?
# crop template in z-direction (for faster processing)
# TODO: refactor to use python module instead of doing i/o
sct.printv('\nCrop data in template space (for faster processing)...', verbose)
ftmp_template_, ftmp_template = ftmp_template, add_suffix(ftmp_template, '_crop')
msct_image.spatial_crop(Image(ftmp_template_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_template)
ftmp_template_seg_, ftmp_template_seg = ftmp_template_seg, add_suffix(ftmp_template_seg, '_crop')
msct_image.spatial_crop(Image(ftmp_template_seg_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_template_seg)
ftmp_data_, ftmp_data = ftmp_data, add_suffix(ftmp_data, '_crop')
msct_image.spatial_crop(Image(ftmp_data_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_data)
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_crop')
msct_image.spatial_crop(Image(ftmp_seg_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_seg)
# sub-sample in z-direction
# TODO: refactor to use python module instead of doing i/o
sct.printv('\nSub-sample in z-direction (for faster processing)...', verbose)
sct.run(['sct_resample', '-i', ftmp_template, '-o', add_suffix(ftmp_template, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_template = add_suffix(ftmp_template, '_sub')
sct.run(['sct_resample', '-i', ftmp_template_seg, '-o', add_suffix(ftmp_template_seg, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_template_seg = add_suffix(ftmp_template_seg, '_sub')
sct.run(['sct_resample', '-i', ftmp_data, '-o', add_suffix(ftmp_data, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_data = add_suffix(ftmp_data, '_sub')
sct.run(['sct_resample', '-i', ftmp_seg, '-o', add_suffix(ftmp_seg, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_seg = add_suffix(ftmp_seg, '_sub')
# Registration straight spinal cord to template
sct.printv('\nRegister straight spinal cord to template...', verbose)
# loop across registration steps
warp_forward = []
warp_inverse = []
for i_step in range(1, len(paramreg.steps)):
sct.printv('\nEstimate transformation for step #' + str(i_step) + '...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_data
dest = ftmp_template
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_seg
dest = ftmp_template_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog':
src_seg = ftmp_seg
dest_seg = ftmp_template_seg
# if step>1, apply warp_forward_concat to the src image to be used
if i_step > 1:
# apply transformation from previous step, to use as new src for registration
sct_apply_transfo.main(args=[
'-i', src,
'-d', dest,
'-w', warp_forward,
'-o', add_suffix(src, '_regStep' + str(i_step - 1)),
'-x', interp_step])
src = add_suffix(src, '_regStep' + str(i_step - 1))
if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog': # also apply transformation to the seg
sct_apply_transfo.main(args=[
'-i', src_seg,
'-d', dest_seg,
'-w', warp_forward,
'-o', add_suffix(src, '_regStep' + str(i_step - 1)),
'-x', interp_step])
src_seg = add_suffix(src_seg, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog': # im_seg case
warp_forward_out, warp_inverse_out = register([src, src_seg], [dest, dest_seg], paramreg, param, str(i_step))
else:
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.append(warp_inverse_out)
# Concatenate transformations: anat --> template
sct.printv('\nConcatenate transformations: anat --> template...', verbose)
warp_forward.insert(0, 'warp_curve2straightAffine.nii.gz')
sct_concat_transfo.main(args=[
'-w', warp_forward,
'-d', 'template.nii',
'-o', 'warp_anat2template.nii.gz'])
# Concatenate transformations: template --> anat
sct.printv('\nConcatenate transformations: template --> anat...', verbose)
warp_inverse.reverse()
if vertebral_alignment:
warp_inverse.append('warp_straight2curve.nii.gz')
sct_concat_transfo.main(args=[
'-w', warp_inverse,
'-d', 'data.nii',
'-o', 'warp_template2anat.nii.gz'])
else:
warp_inverse.append('straight2templateAffine.txt')
warp_inverse.append('warp_straight2curve.nii.gz')
sct_concat_transfo.main(args=[
'-w', warp_inverse,
'-winv', ['straight2templateAffine.txt'],
'-d', 'data.nii',
'-o', 'warp_template2anat.nii.gz'])
# register template->subject
elif ref == 'subject':
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
ftmp_data = Image(ftmp_data).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_seg = Image(ftmp_seg).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_label = Image(ftmp_label).change_orientation("RPI", generate_path=True).save().absolutepath
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run(['sct_label_utils', '-i', ftmp_template_label, '-o', ftmp_template_label, '-remove-reference', ftmp_label])
# Add one label because at least 3 orthogonal labels are required to estimate an affine transformation. This
# new label is added at the level of the upper most label (lowest value), at 1cm to the right.
for i_file in [ftmp_label, ftmp_template_label]:
im_label = Image(i_file)
coord_label = im_label.getCoordinatesAveragedByValue() # N.B. landmarks are sorted by value
# Create new label
from copy import deepcopy
new_label = deepcopy(coord_label[0])
# move it 5mm to the left (orientation is RAS)
nx, ny, nz, nt, px, py, pz, pt = im_label.dim
new_label.x = np.round(coord_label[0].x + 5.0 / px)
# assign value 99
new_label.value = 99
# Add to existing image
im_label.data[int(new_label.x), int(new_label.y), int(new_label.z)] = new_label.value
# Overwrite label file
# im_label.absolutepath = 'label_rpi_modif.nii.gz'
im_label.save()
# Bring template to subject space using landmark-based transformation
sct.printv('\nEstimate transformation for step #0...', verbose)
warp_forward = ['template2subjectAffine.txt']
warp_inverse = ['template2subjectAffine.txt']
try:
register_landmarks(ftmp_template_label, ftmp_label, paramreg.steps['0'].dof, fname_affine=warp_forward[0], verbose=verbose, path_qc="./")
except Exception:
sct.printv('ERROR: input labels do not seem to be at the right place. Please check the position of the labels. See documentation for more details: https://www.slideshare.net/neuropoly/sct-course-20190121/42', verbose=verbose, type='error')
# loop across registration steps
for i_step in range(1, len(paramreg.steps)):
sct.printv('\nEstimate transformation for step #' + str(i_step) + '...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_template
dest = ftmp_data
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_template_seg
dest = ftmp_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# apply transformation from previous step, to use as new src for registration
sct_apply_transfo.main(args=[
'-i', src,
'-d', dest,
'-w', warp_forward,
'-o', add_suffix(src, '_regStep' + str(i_step - 1)),
'-x', interp_step])
src = add_suffix(src, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.insert(0, warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: template --> subject...', verbose)
sct_concat_transfo.main(args=[
'-w', warp_forward,
'-d', 'data.nii',
'-o', 'warp_template2anat.nii.gz'])
sct.printv('\nConcatenate transformations: subject --> template...', verbose)
sct_concat_transfo.main(args=[
'-w', warp_inverse,
'-winv', ['template2subjectAffine.txt'],
'-d', 'template.nii',
'-o', 'warp_anat2template.nii.gz'])
# Apply warping fields to anat and template
sct.run(['sct_apply_transfo', '-i', 'template.nii', '-o', 'template2anat.nii.gz', '-d', 'data.nii', '-w', 'warp_template2anat.nii.gz', '-crop', '1'], verbose)
sct.run(['sct_apply_transfo', '-i', 'data.nii', '-o', 'anat2template.nii.gz', '-d', 'template.nii', '-w', 'warp_anat2template.nii.gz', '-crop', '1'], verbose)
# come back
os.chdir(curdir)
# Generate output files
sct.printv('\nGenerate output files...', verbose)
fname_template2anat = os.path.join(path_output, 'template2anat' + ext_data)
fname_anat2template = os.path.join(path_output, 'anat2template' + ext_data)
sct.generate_output_file(os.path.join(path_tmp, "warp_template2anat.nii.gz"), os.path.join(path_output, "warp_template2anat.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "warp_anat2template.nii.gz"), os.path.join(path_output, "warp_anat2template.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "template2anat.nii.gz"), fname_template2anat, verbose)
sct.generate_output_file(os.path.join(path_tmp, "anat2template.nii.gz"), fname_anat2template, verbose)
if ref == 'template':
# copy straightening files in case subsequent SCT functions need them
sct.generate_output_file(os.path.join(path_tmp, "warp_curve2straight.nii.gz"), os.path.join(path_output, "warp_curve2straight.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "warp_straight2curve.nii.gz"), os.path.join(path_output, "warp_straight2curve.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "straight_ref.nii.gz"), os.path.join(path_output, "straight_ref.nii.gz"), verbose)
# Delete temporary files
if param.remove_temp_files:
sct.printv('\nDelete temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's', verbose)
qc_dataset = arguments.get("-qc-dataset", None)
qc_subject = arguments.get("-qc-subject", None)
if param.path_qc is not None:
generate_qc(fname_data, fname_in2=fname_template2anat, fname_seg=fname_seg, args=args,
path_qc=os.path.abspath(param.path_qc), dataset=qc_dataset, subject=qc_subject,
process='sct_register_to_template')
sct.display_viewer_syntax([fname_data, fname_template2anat], verbose=verbose)
sct.display_viewer_syntax([fname_template, fname_anat2template], verbose=verbose)
def main():
# Default params
param = Param()
# Get parser info
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
fname_data = arguments['-i']
if '-m' in arguments:
fname_mask = arguments['-m']
else:
fname_mask = ''
method = arguments["-method"]
if '-vol' in arguments:
index_vol_user = arguments['-vol']
else:
index_vol_user = ''
# Check parameters
if method == 'diff':
if not fname_mask:
sct.printv('You need to provide a mask with -method diff. Exit.',
1,
type='error')
# Load data and orient to RPI
im_data = Image(fname_data).change_orientation('RPI')
data = im_data.data
if fname_mask:
mask = Image(fname_mask).change_orientation('RPI').data
# Retrieve selected volumes
if index_vol_user:
index_vol = parse_num_list(index_vol_user)
else:
index_vol = range(data.shape[3])
# Make sure user selected 2 volumes with diff method
if method == 'diff':
if not len(index_vol) == 2:
sct.printv(
'Method "diff" should be used with exactly two volumes (specify with flag "-vol").',
1, 'error')
# Compute SNR
# NB: "time" is assumed to be the 4th dimension of the variable "data"
if method == 'mult':
# Compute mean and STD across time
data_mean = np.mean(data[:, :, :, index_vol], axis=3)
data_std = np.std(data[:, :, :, index_vol], axis=3)
# Generate mask where std is different from 0
mask_std_nonzero = np.where(data_std > param.almost_zero)
snr_map = np.zeros_like(data_mean)
snr_map[mask_std_nonzero] = data_mean[mask_std_nonzero] / data_std[
mask_std_nonzero]
# Output SNR map
fname_snr = sct.add_suffix(fname_data, '_SNR-' + method)
im_snr = empty_like(im_data)
im_snr.data = snr_map
im_snr.save(fname_snr, dtype=np.float32)
# Output non-zero mask
fname_stdnonzero = sct.add_suffix(fname_data,
'_mask-STD-nonzero' + method)
im_stdnonzero = empty_like(im_data)
data_stdnonzero = np.zeros_like(data_mean)
data_stdnonzero[mask_std_nonzero] = 1
im_stdnonzero.data = data_stdnonzero
im_stdnonzero.save(fname_stdnonzero, dtype=np.float32)
# Compute SNR in ROI
if fname_mask:
mean_in_roi = np.average(data_mean[mask_std_nonzero],
weights=mask[mask_std_nonzero])
std_in_roi = np.average(data_std[mask_std_nonzero],
weights=mask[mask_std_nonzero])
snr_roi = mean_in_roi / std_in_roi
# snr_roi = np.average(snr_map[mask_std_nonzero], weights=mask[mask_std_nonzero])
elif method == 'diff':
data_2vol = np.take(data, index_vol, axis=3)
# Compute mean in ROI
data_mean = np.mean(data_2vol, axis=3)
mean_in_roi = np.average(data_mean, weights=mask)
data_sub = np.subtract(data_2vol[:, :, :, 1], data_2vol[:, :, :, 0])
_, std_in_roi = weighted_avg_and_std(data_sub, mask)
# Compute SNR, correcting for Rayleigh noise (see eq. 7 in Dietrich et al.)
snr_roi = (2 / np.sqrt(2)) * mean_in_roi / std_in_roi
# Display result
if fname_mask:
sct.printv('\nSNR_' + method + ' = ' + str(snr_roi) + '\n',
type='info')
def main(args=None):
# initializations
param = Param()
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(args)
fname_data = arguments['-i']
fname_seg = arguments['-s']
if '-l' in arguments:
fname_landmarks = arguments['-l']
label_type = 'body'
elif '-ldisc' in arguments:
fname_landmarks = arguments['-ldisc']
label_type = 'disc'
else:
sct.printv('ERROR: Labels should be provided.', 1, 'error')
if '-ofolder' in arguments:
path_output = arguments['-ofolder']
else:
path_output = ''
param.path_qc = arguments.get("-qc", None)
path_template = arguments['-t']
contrast_template = arguments['-c']
ref = arguments['-ref']
param.remove_temp_files = int(arguments.get('-r'))
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
param.verbose = verbose # TODO: not clean, unify verbose or param.verbose in code, but not both
param.straighten_fitting = arguments['-straighten-fitting']
# if '-cpu-nb' in arguments:
# arg_cpu = ' -cpu-nb '+str(arguments['-cpu-nb'])
# else:
# arg_cpu = ''
# registration parameters
if '-param' in arguments:
# reset parameters but keep step=0 (might be overwritten if user specified step=0)
paramreg = ParamregMultiStep([step0])
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# add user parameters
for paramStep in arguments['-param']:
paramreg.addStep(paramStep)
else:
paramreg = ParamregMultiStep([step0, step1, step2])
# if ref=subject, initialize registration using different affine parameters
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# initialize other parameters
zsubsample = param.zsubsample
# retrieve template file names
file_template_vertebral_labeling = get_file_label(os.path.join(path_template, 'template'), 'vertebral labeling')
file_template = get_file_label(os.path.join(path_template, 'template'), contrast_template.upper() + '-weighted template')
file_template_seg = get_file_label(os.path.join(path_template, 'template'), 'spinal cord')
# start timer
start_time = time.time()
# get fname of the template + template objects
fname_template = os.path.join(path_template, 'template', file_template)
fname_template_vertebral_labeling = os.path.join(path_template, 'template', file_template_vertebral_labeling)
fname_template_seg = os.path.join(path_template, 'template', file_template_seg)
fname_template_disc_labeling = os.path.join(path_template, 'template', 'PAM50_label_disc.nii.gz')
# check file existence
# TODO: no need to do that!
sct.printv('\nCheck template files...')
sct.check_file_exist(fname_template, verbose)
sct.check_file_exist(fname_template_vertebral_labeling, verbose)
sct.check_file_exist(fname_template_seg, verbose)
path_data, file_data, ext_data = sct.extract_fname(fname_data)
# sct.printv(arguments)
sct.printv('\nCheck parameters:', verbose)
sct.printv(' Data: ' + fname_data, verbose)
sct.printv(' Landmarks: ' + fname_landmarks, verbose)
sct.printv(' Segmentation: ' + fname_seg, verbose)
sct.printv(' Path template: ' + path_template, verbose)
sct.printv(' Remove temp files: ' + str(param.remove_temp_files), verbose)
# check input labels
labels = check_labels(fname_landmarks, label_type=label_type)
vertebral_alignment = False
if len(labels) > 2 and label_type == 'disc':
vertebral_alignment = True
path_tmp = sct.tmp_create(basename="register_to_template", verbose=verbose)
# set temporary file names
ftmp_data = 'data.nii'
ftmp_seg = 'seg.nii.gz'
ftmp_label = 'label.nii.gz'
ftmp_template = 'template.nii'
ftmp_template_seg = 'template_seg.nii.gz'
ftmp_template_label = 'template_label.nii.gz'
# copy files to temporary folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
Image(fname_data).save(os.path.join(path_tmp, ftmp_data))
Image(fname_seg).save(os.path.join(path_tmp, ftmp_seg))
Image(fname_landmarks).save(os.path.join(path_tmp, ftmp_label))
Image(fname_template).save(os.path.join(path_tmp, ftmp_template))
Image(fname_template_seg).save(os.path.join(path_tmp, ftmp_template_seg))
Image(fname_template_vertebral_labeling).save(os.path.join(path_tmp, ftmp_template_label))
if label_type == 'disc':
Image(fname_template_disc_labeling).save(os.path.join(path_tmp, ftmp_template_label))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Generate labels from template vertebral labeling
if label_type == 'body':
sct.printv('\nGenerate labels from template vertebral labeling', verbose)
ftmp_template_label_, ftmp_template_label = ftmp_template_label, sct.add_suffix(ftmp_template_label, "_body")
sct_label_utils.main(args=['-i', ftmp_template_label_, '-vert-body', '0', '-o', ftmp_template_label])
# check if provided labels are available in the template
sct.printv('\nCheck if provided labels are available in the template', verbose)
image_label_template = Image(ftmp_template_label)
labels_template = image_label_template.getNonZeroCoordinates(sorting='value')
if labels[-1].value > labels_template[-1].value:
sct.printv('ERROR: Wrong landmarks input. Labels must have correspondence in template space. \nLabel max '
'provided: ' + str(labels[-1].value) + '\nLabel max from template: ' +
str(labels_template[-1].value), verbose, 'error')
# if only one label is present, force affine transformation to be Tx,Ty,Tz only (no scaling)
if len(labels) == 1:
paramreg.steps['0'].dof = 'Tx_Ty_Tz'
sct.printv('WARNING: Only one label is present. Forcing initial transformation to: ' + paramreg.steps['0'].dof,
1, 'warning')
# Project labels onto the spinal cord centerline because later, an affine transformation is estimated between the
# template's labels (centered in the cord) and the subject's labels (assumed to be centered in the cord).
# If labels are not centered, mis-registration errors are observed (see issue #1826)
ftmp_label = project_labels_on_spinalcord(ftmp_label, ftmp_seg)
# binarize segmentation (in case it has values below 0 caused by manual editing)
sct.printv('\nBinarize segmentation', verbose)
ftmp_seg_, ftmp_seg = ftmp_seg, sct.add_suffix(ftmp_seg, "_bin")
sct_maths.main(['-i', ftmp_seg_,
'-bin', '0.5',
'-o', ftmp_seg])
# Switch between modes: subject->template or template->subject
if ref == 'template':
# resample data to 1mm isotropic
sct.printv('\nResample data to 1mm isotropic...', verbose)
resample_file(ftmp_data, add_suffix(ftmp_data, '_1mm'), '1.0x1.0x1.0', 'mm', 'linear', verbose)
ftmp_data = add_suffix(ftmp_data, '_1mm')
resample_file(ftmp_seg, add_suffix(ftmp_seg, '_1mm'), '1.0x1.0x1.0', 'mm', 'linear', verbose)
ftmp_seg = add_suffix(ftmp_seg, '_1mm')
# N.B. resampling of labels is more complicated, because they are single-point labels, therefore resampling
# with nearest neighbour can make them disappear.
resample_labels(ftmp_label, ftmp_data, add_suffix(ftmp_label, '_1mm'))
ftmp_label = add_suffix(ftmp_label, '_1mm')
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
ftmp_data = Image(ftmp_data).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_seg = Image(ftmp_seg).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_label = Image(ftmp_label).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_crop')
if vertebral_alignment:
# cropping the segmentation based on the label coverage to ensure good registration with vertebral alignment
# See https://github.com/neuropoly/spinalcordtoolbox/pull/1669 for details
image_labels = Image(ftmp_label)
coordinates_labels = image_labels.getNonZeroCoordinates(sorting='z')
nx, ny, nz, nt, px, py, pz, pt = image_labels.dim
offset_crop = 10.0 * pz # cropping the image 10 mm above and below the highest and lowest label
cropping_slices = [coordinates_labels[0].z - offset_crop, coordinates_labels[-1].z + offset_crop]
# make sure that the cropping slices do not extend outside of the slice range (issue #1811)
if cropping_slices[0] < 0:
cropping_slices[0] = 0
if cropping_slices[1] > nz:
cropping_slices[1] = nz
msct_image.spatial_crop(Image(ftmp_seg_), dict(((2, np.int32(np.round(cropping_slices))),))).save(ftmp_seg)
else:
# if we do not align the vertebral levels, we crop the segmentation from top to bottom
im_seg_rpi = Image(ftmp_seg_)
bottom = 0
for data in msct_image.SlicerOneAxis(im_seg_rpi, "IS"):
if (data != 0).any():
break
bottom += 1
top = im_seg_rpi.data.shape[2]
for data in msct_image.SlicerOneAxis(im_seg_rpi, "SI"):
if (data != 0).any():
break
top -= 1
msct_image.spatial_crop(im_seg_rpi, dict(((2, (bottom, top)),))).save(ftmp_seg)
# straighten segmentation
sct.printv('\nStraighten the spinal cord using centerline/segmentation...', verbose)
# check if warp_curve2straight and warp_straight2curve already exist (i.e. no need to do it another time)
fn_warp_curve2straight = os.path.join(curdir, "warp_curve2straight.nii.gz")
fn_warp_straight2curve = os.path.join(curdir, "warp_straight2curve.nii.gz")
fn_straight_ref = os.path.join(curdir, "straight_ref.nii.gz")
cache_input_files=[ftmp_seg]
if vertebral_alignment:
cache_input_files += [
ftmp_template_seg,
ftmp_label,
ftmp_template_label,
]
cache_sig = sct.cache_signature(
input_files=cache_input_files,
)
cachefile = os.path.join(curdir, "straightening.cache")
if sct.cache_valid(cachefile, cache_sig) and os.path.isfile(fn_warp_curve2straight) and os.path.isfile(fn_warp_straight2curve) and os.path.isfile(fn_straight_ref):
sct.printv('Reusing existing warping field which seems to be valid', verbose, 'warning')
sct.copy(fn_warp_curve2straight, 'warp_curve2straight.nii.gz')
sct.copy(fn_warp_straight2curve, 'warp_straight2curve.nii.gz')
sct.copy(fn_straight_ref, 'straight_ref.nii.gz')
# apply straightening
sct.run(['sct_apply_transfo', '-i', ftmp_seg, '-w', 'warp_curve2straight.nii.gz', '-d', 'straight_ref.nii.gz', '-o', add_suffix(ftmp_seg, '_straight')])
else:
from spinalcordtoolbox.straightening import SpinalCordStraightener
sc_straight = SpinalCordStraightener(ftmp_seg, ftmp_seg)
sc_straight.algo_fitting = param.straighten_fitting
sc_straight.output_filename = add_suffix(ftmp_seg, '_straight')
sc_straight.path_output = './'
sc_straight.qc = '0'
sc_straight.remove_temp_files = param.remove_temp_files
sc_straight.verbose = verbose
if vertebral_alignment:
sc_straight.centerline_reference_filename = ftmp_template_seg
sc_straight.use_straight_reference = True
sc_straight.discs_input_filename = ftmp_label
sc_straight.discs_ref_filename = ftmp_template_label
sc_straight.straighten()
sct.cache_save(cachefile, cache_sig)
# N.B. DO NOT UPDATE VARIABLE ftmp_seg BECAUSE TEMPORARY USED LATER
# re-define warping field using non-cropped space (to avoid issue #367)
s, o = sct.run(['sct_concat_transfo', '-w', 'warp_straight2curve.nii.gz', '-d', ftmp_data, '-o', 'warp_straight2curve.nii.gz'])
if vertebral_alignment:
sct.copy('warp_curve2straight.nii.gz', 'warp_curve2straightAffine.nii.gz')
else:
# Label preparation:
# --------------------------------------------------------------------------------
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run(['sct_label_utils', '-i', ftmp_template_label, '-o', ftmp_template_label, '-remove-reference', ftmp_label])
# Dilating the input label so they can be straighten without losing them
sct.printv('\nDilating input labels using 3vox ball radius')
sct_maths.main(['-i', ftmp_label,
'-dilate', '3',
'-o', add_suffix(ftmp_label, '_dilate')])
ftmp_label = add_suffix(ftmp_label, '_dilate')
# Apply straightening to labels
sct.printv('\nApply straightening to labels...', verbose)
sct.run(['sct_apply_transfo', '-i', ftmp_label, '-o', add_suffix(ftmp_label, '_straight'), '-d', add_suffix(ftmp_seg, '_straight'), '-w', 'warp_curve2straight.nii.gz', '-x', 'nn'])
ftmp_label = add_suffix(ftmp_label, '_straight')
# Compute rigid transformation straight landmarks --> template landmarks
sct.printv('\nEstimate transformation for step #0...', verbose)
try:
register_landmarks(ftmp_label, ftmp_template_label, paramreg.steps['0'].dof,
fname_affine='straight2templateAffine.txt', verbose=verbose)
except RuntimeError:
raise('Input labels do not seem to be at the right place. Please check the position of the labels. '
'See documentation for more details: https://www.slideshare.net/neuropoly/sct-course-20190121/42')
# Concatenate transformations: curve --> straight --> affine
sct.printv('\nConcatenate transformations: curve --> straight --> affine...', verbose)
sct.run(['sct_concat_transfo', '-w', 'warp_curve2straight.nii.gz,straight2templateAffine.txt', '-d', 'template.nii', '-o', 'warp_curve2straightAffine.nii.gz'])
# Apply transformation
sct.printv('\nApply transformation...', verbose)
sct.run(['sct_apply_transfo', '-i', ftmp_data, '-o', add_suffix(ftmp_data, '_straightAffine'), '-d', ftmp_template, '-w', 'warp_curve2straightAffine.nii.gz'])
ftmp_data = add_suffix(ftmp_data, '_straightAffine')
sct.run(['sct_apply_transfo', '-i', ftmp_seg, '-o', add_suffix(ftmp_seg, '_straightAffine'), '-d', ftmp_template, '-w', 'warp_curve2straightAffine.nii.gz', '-x', 'linear'])
ftmp_seg = add_suffix(ftmp_seg, '_straightAffine')
"""
# Benjamin: Issue from Allan Martin, about the z=0 slice that is screwed up, caused by the affine transform.
# Solution found: remove slices below and above landmarks to avoid rotation effects
points_straight = []
for coord in landmark_template:
points_straight.append(coord.z)
min_point, max_point = int(np.round(np.min(points_straight))), int(np.round(np.max(points_straight)))
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_black')
msct_image.spatial_crop(Image(ftmp_seg_), dict(((2, (min_point,max_point)),))).save(ftmp_seg)
"""
# open segmentation
im = Image(ftmp_seg)
im_new = msct_image.empty_like(im)
# binarize
im_new.data = im.data > 0.5
# find min-max of anat2template (for subsequent cropping)
zmin_template, zmax_template = msct_image.find_zmin_zmax(im_new, threshold=0.5)
# save binarized segmentation
im_new.save(add_suffix(ftmp_seg, '_bin')) # unused?
# crop template in z-direction (for faster processing)
# TODO: refactor to use python module instead of doing i/o
sct.printv('\nCrop data in template space (for faster processing)...', verbose)
ftmp_template_, ftmp_template = ftmp_template, add_suffix(ftmp_template, '_crop')
msct_image.spatial_crop(Image(ftmp_template_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_template)
ftmp_template_seg_, ftmp_template_seg = ftmp_template_seg, add_suffix(ftmp_template_seg, '_crop')
msct_image.spatial_crop(Image(ftmp_template_seg_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_template_seg)
ftmp_data_, ftmp_data = ftmp_data, add_suffix(ftmp_data, '_crop')
msct_image.spatial_crop(Image(ftmp_data_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_data)
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_crop')
msct_image.spatial_crop(Image(ftmp_seg_), dict(((2, (zmin_template,zmax_template)),))).save(ftmp_seg)
# sub-sample in z-direction
# TODO: refactor to use python module instead of doing i/o
sct.printv('\nSub-sample in z-direction (for faster processing)...', verbose)
sct.run(['sct_resample', '-i', ftmp_template, '-o', add_suffix(ftmp_template, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_template = add_suffix(ftmp_template, '_sub')
sct.run(['sct_resample', '-i', ftmp_template_seg, '-o', add_suffix(ftmp_template_seg, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_template_seg = add_suffix(ftmp_template_seg, '_sub')
sct.run(['sct_resample', '-i', ftmp_data, '-o', add_suffix(ftmp_data, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_data = add_suffix(ftmp_data, '_sub')
sct.run(['sct_resample', '-i', ftmp_seg, '-o', add_suffix(ftmp_seg, '_sub'), '-f', '1x1x' + zsubsample], verbose)
ftmp_seg = add_suffix(ftmp_seg, '_sub')
# Registration straight spinal cord to template
sct.printv('\nRegister straight spinal cord to template...', verbose)
# loop across registration steps
warp_forward = []
warp_inverse = []
for i_step in range(1, len(paramreg.steps)):
sct.printv('\nEstimate transformation for step #' + str(i_step) + '...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_data
dest = ftmp_template
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_seg
dest = ftmp_template_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog':
src_seg = ftmp_seg
dest_seg = ftmp_template_seg
# if step>1, apply warp_forward_concat to the src image to be used
if i_step > 1:
# apply transformation from previous step, to use as new src for registration
sct.run(['sct_apply_transfo', '-i', src, '-d', dest, '-w', ','.join(warp_forward), '-o', add_suffix(src, '_regStep' + str(i_step - 1)), '-x', interp_step], verbose)
src = add_suffix(src, '_regStep' + str(i_step - 1))
if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog': # also apply transformation to the seg
sct.run(['sct_apply_transfo', '-i', src_seg, '-d', dest_seg, '-w', ','.join(warp_forward), '-o', add_suffix(src, '_regStep' + str(i_step - 1)), '-x', interp_step], verbose)
src_seg = add_suffix(src_seg, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog': # im_seg case
warp_forward_out, warp_inverse_out = register([src, src_seg], [dest, dest_seg], paramreg, param, str(i_step))
else:
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.append(warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: anat --> template...', verbose)
sct.run(['sct_concat_transfo', '-w', 'warp_curve2straightAffine.nii.gz,' + ','.join(warp_forward), '-d', 'template.nii', '-o', 'warp_anat2template.nii.gz'], verbose)
# sct.run('sct_concat_transfo -w warp_curve2straight.nii.gz,straight2templateAffine.txt,'+','.join(warp_forward)+' -d template.nii -o warp_anat2template.nii.gz', verbose)
sct.printv('\nConcatenate transformations: template --> anat...', verbose)
warp_inverse.reverse()
if vertebral_alignment:
sct.run(['sct_concat_transfo', '-w', ','.join(warp_inverse) + ',warp_straight2curve.nii.gz', '-d', 'data.nii', '-o', 'warp_template2anat.nii.gz'], verbose)
else:
sct.run(['sct_concat_transfo', '-w', ','.join(warp_inverse) + ',-straight2templateAffine.txt,warp_straight2curve.nii.gz', '-d', 'data.nii', '-o', 'warp_template2anat.nii.gz'], verbose)
# register template->subject
elif ref == 'subject':
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
ftmp_data = Image(ftmp_data).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_seg = Image(ftmp_seg).change_orientation("RPI", generate_path=True).save().absolutepath
ftmp_label = Image(ftmp_label).change_orientation("RPI", generate_path=True).save().absolutepath
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run(['sct_label_utils', '-i', ftmp_template_label, '-o', ftmp_template_label, '-remove-reference', ftmp_label])
# Add one label because at least 3 orthogonal labels are required to estimate an affine transformation. This
# new label is added at the level of the upper most label (lowest value), at 1cm to the right.
for i_file in [ftmp_label, ftmp_template_label]:
im_label = Image(i_file)
coord_label = im_label.getCoordinatesAveragedByValue() # N.B. landmarks are sorted by value
# Create new label
from copy import deepcopy
new_label = deepcopy(coord_label[0])
# move it 5mm to the left (orientation is RAS)
nx, ny, nz, nt, px, py, pz, pt = im_label.dim
new_label.x = np.round(coord_label[0].x + 5.0 / px)
# assign value 99
new_label.value = 99
# Add to existing image
im_label.data[int(new_label.x), int(new_label.y), int(new_label.z)] = new_label.value
# Overwrite label file
# im_label.absolutepath = 'label_rpi_modif.nii.gz'
im_label.save()
# Bring template to subject space using landmark-based transformation
sct.printv('\nEstimate transformation for step #0...', verbose)
warp_forward = ['template2subjectAffine.txt']
warp_inverse = ['-template2subjectAffine.txt']
try:
register_landmarks(ftmp_template_label, ftmp_label, paramreg.steps['0'].dof, fname_affine=warp_forward[0], verbose=verbose, path_qc="./")
except Exception:
sct.printv('ERROR: input labels do not seem to be at the right place. Please check the position of the labels. See documentation for more details: https://www.slideshare.net/neuropoly/sct-course-20190121/42', verbose=verbose, type='error')
# loop across registration steps
for i_step in range(1, len(paramreg.steps)):
sct.printv('\nEstimate transformation for step #' + str(i_step) + '...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_template
dest = ftmp_data
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_template_seg
dest = ftmp_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# apply transformation from previous step, to use as new src for registration
sct.run(['sct_apply_transfo', '-i', src, '-d', dest, '-w', ','.join(warp_forward), '-o', add_suffix(src, '_regStep' + str(i_step - 1)), '-x', interp_step], verbose)
src = add_suffix(src, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.insert(0, warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: template --> subject...', verbose)
sct.run(['sct_concat_transfo', '-w', ','.join(warp_forward), '-d', 'data.nii', '-o', 'warp_template2anat.nii.gz'], verbose)
sct.printv('\nConcatenate transformations: subject --> template...', verbose)
sct.run(['sct_concat_transfo', '-w', ','.join(warp_inverse), '-d', 'template.nii', '-o', 'warp_anat2template.nii.gz'], verbose)
# Apply warping fields to anat and template
sct.run(['sct_apply_transfo', '-i', 'template.nii', '-o', 'template2anat.nii.gz', '-d', 'data.nii', '-w', 'warp_template2anat.nii.gz', '-crop', '1'], verbose)
sct.run(['sct_apply_transfo', '-i', 'data.nii', '-o', 'anat2template.nii.gz', '-d', 'template.nii', '-w', 'warp_anat2template.nii.gz', '-crop', '1'], verbose)
# come back
os.chdir(curdir)
# Generate output files
sct.printv('\nGenerate output files...', verbose)
fname_template2anat = os.path.join(path_output, 'template2anat' + ext_data)
fname_anat2template = os.path.join(path_output, 'anat2template' + ext_data)
sct.generate_output_file(os.path.join(path_tmp, "warp_template2anat.nii.gz"), os.path.join(path_output, "warp_template2anat.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "warp_anat2template.nii.gz"), os.path.join(path_output, "warp_anat2template.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "template2anat.nii.gz"), fname_template2anat, verbose)
sct.generate_output_file(os.path.join(path_tmp, "anat2template.nii.gz"), fname_anat2template, verbose)
if ref == 'template':
# copy straightening files in case subsequent SCT functions need them
sct.generate_output_file(os.path.join(path_tmp, "warp_curve2straight.nii.gz"), os.path.join(path_output, "warp_curve2straight.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "warp_straight2curve.nii.gz"), os.path.join(path_output, "warp_straight2curve.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "straight_ref.nii.gz"), os.path.join(path_output, "straight_ref.nii.gz"), verbose)
# Delete temporary files
if param.remove_temp_files:
sct.printv('\nDelete temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's', verbose)
qc_dataset = arguments.get("-qc-dataset", None)
qc_subject = arguments.get("-qc-subject", None)
if param.path_qc is not None:
generate_qc(fname_data, fname_in2=fname_template2anat, fname_seg=fname_seg, args=args,
path_qc=os.path.abspath(param.path_qc), dataset=qc_dataset, subject=qc_subject,
process='sct_register_to_template')
sct.display_viewer_syntax([fname_data, fname_template2anat], verbose=verbose)
sct.display_viewer_syntax([fname_template, fname_anat2template], verbose=verbose)
def create_mask(param):
# parse argument for method
method_type = param.process[0]
# check method val
if not method_type == 'center':
method_val = param.process[1]
# check existence of input files
if method_type == 'centerline':
sct.check_file_exist(method_val, param.verbose)
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
# Get output folder and file name
if param.fname_out == '':
param.fname_out = os.path.abspath(param.file_prefix + file_data + ext_data)
path_tmp = sct.tmp_create(basename="create_mask", verbose=param.verbose)
sct.printv('\nOrientation:', param.verbose)
orientation_input = Image(param.fname_data).orientation
sct.printv(' ' + orientation_input, param.verbose)
# copy input data to tmp folder and re-orient to RPI
Image(param.fname_data).change_orientation("RPI").save(os.path.join(path_tmp, "data_RPI.nii"))
if method_type == 'centerline':
Image(method_val).change_orientation("RPI").save(os.path.join(path_tmp, "centerline_RPI.nii"))
if method_type == 'point':
Image(method_val).change_orientation("RPI").save(os.path.join(path_tmp, "point_RPI.nii"))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Get dimensions of data
im_data = Image('data_RPI.nii')
nx, ny, nz, nt, px, py, pz, pt = im_data.dim
sct.printv('\nDimensions:', param.verbose)
sct.printv(im_data.dim, param.verbose)
# in case user input 4d data
if nt != 1:
sct.printv('WARNING in ' + os.path.basename(__file__) + ': Input image is 4d but output mask will be 3D from first time slice.', param.verbose, 'warning')
# extract first volume to have 3d reference
nii = msct_image.empty_like(Image('data_RPI.nii'))
data3d = nii.data[:, :, :, 0]
nii.data = data3d
nii.save('data_RPI.nii')
if method_type == 'coord':
# parse to get coordinate
coord = [x for x in map(int, method_val.split('x'))]
if method_type == 'point':
# get file name
# extract coordinate of point
sct.printv('\nExtract coordinate of point...', param.verbose)
# TODO: change this way to remove dependence to sct.run. ProcessLabels.display_voxel returns list of coordinates
status, output = sct.run(['sct_label_utils', '-i', 'point_RPI.nii', '-display'], verbose=param.verbose)
# parse to get coordinate
# TODO fixup... this is quite magic
coord = output[output.find('Position=') + 10:-17].split(',')
if method_type == 'center':
# set coordinate at center of FOV
coord = np.round(float(nx) / 2), np.round(float(ny) / 2)
if method_type == 'centerline':
# get name of centerline from user argument
fname_centerline = 'centerline_RPI.nii'
else:
# generate volume with line along Z at coordinates 'coord'
sct.printv('\nCreate line...', param.verbose)
fname_centerline = create_line(param, 'data_RPI.nii', coord, nz)
# create mask
sct.printv('\nCreate mask...', param.verbose)
centerline = nibabel.load(fname_centerline) # open centerline
hdr = centerline.get_header() # get header
hdr.set_data_dtype('uint8') # set imagetype to uint8
spacing = hdr.structarr['pixdim']
data_centerline = centerline.get_data() # get centerline
# if data is 2D, reshape with empty third dimension
if len(data_centerline.shape) == 2:
data_centerline_shape = list(data_centerline.shape)
data_centerline_shape.append(1)
data_centerline = data_centerline.reshape(data_centerline_shape)
z_centerline_not_null = [iz for iz in range(0, nz, 1) if data_centerline[:, :, iz].any()]
# get center of mass of the centerline
cx = [0] * nz
cy = [0] * nz
for iz in range(0, nz, 1):
if iz in z_centerline_not_null:
cx[iz], cy[iz] = ndimage.measurements.center_of_mass(np.array(data_centerline[:, :, iz]))
# create 2d masks
file_mask = 'data_mask'
for iz in range(nz):
if iz not in z_centerline_not_null:
# write an empty nifty volume
img = nibabel.Nifti1Image(data_centerline[:, :, iz], None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
else:
center = np.array([cx[iz], cy[iz]])
mask2d = create_mask2d(param, center, param.shape, param.size, im_data=im_data)
# Write NIFTI volumes
img = nibabel.Nifti1Image(mask2d, None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
fname_list = [file_mask + str(iz) + '.nii' for iz in range(nz)]
im_out = concat_data(fname_list, dim=2).save('mask_RPI.nii.gz')
im_out.change_orientation(orientation_input)
im_out.header = Image(param.fname_data).header
im_out.save(param.fname_out)
# come back
os.chdir(curdir)
# Remove temporary files
if param.remove_temp_files == 1:
sct.printv('\nRemove temporary files...', param.verbose)
sct.rmtree(path_tmp)
sct.display_viewer_syntax([param.fname_data, param.fname_out], colormaps=['gray', 'red'], opacities=['', '0.5'])
def main(argv=None):
parser = get_parser()
arguments = parser.parse_args(argv)
verbose = arguments.v
set_loglevel(verbose=verbose)
# Default params
param = Param()
# Get parser info
fname_data = arguments.i
fname_mask = arguments.m
fname_mask_noise = arguments.m_noise
method = arguments.method
file_name = arguments.o
rayleigh_correction = arguments.rayleigh
# Check parameters
if method in ['diff', 'single']:
if not fname_mask:
raise parser.error(
f"Argument '-m' must be specified when using '-method {method}'."
)
# Load data
im_data = Image(fname_data)
data = im_data.data
dim = len(data.shape)
nz = data.shape[2]
if fname_mask:
mask = Image(fname_mask).data
# Check dimensionality
if method in ['diff', 'mult']:
if dim != 4:
raise ValueError(
f"Input data dimension: {dim}. Input dimension for this method should be 4."
)
if method in ['single']:
if dim not in [3, 4]:
raise ValueError(
f"Input data dimension: {dim}. Input dimension for this method should be 3 or 4."
)
# Check dimensionality of mask
if fname_mask:
if len(mask.shape) != 3:
raise ValueError(
f"Mask should be a 3D image, but the input mask has shape '{mask.shape}'."
)
# Retrieve selected volumes
index_vol = parse_num_list(arguments.vol)
if not index_vol:
if method == 'mult':
index_vol = range(data.shape[3])
elif method == 'diff':
index_vol = [0, 1]
elif method == 'single':
index_vol = [0]
# Compute SNR
# NB: "time" is assumed to be the 4th dimension of the variable "data"
if method == 'mult':
# Compute mean and STD across time
data_mean = np.mean(data[:, :, :, index_vol], axis=3)
data_std = np.std(data[:, :, :, index_vol], axis=3, ddof=1)
# Generate mask where std is different from 0
mask_std_nonzero = np.where(data_std > param.almost_zero)
snr_map = np.zeros_like(data_mean)
snr_map[mask_std_nonzero] = data_mean[mask_std_nonzero] / data_std[
mask_std_nonzero]
# Output SNR map
fname_snr = add_suffix(fname_data, '_SNR-' + method)
im_snr = empty_like(im_data)
im_snr.data = snr_map
im_snr.save(fname_snr, dtype=np.float32)
# Output non-zero mask
fname_stdnonzero = add_suffix(fname_data, '_mask-STD-nonzero' + method)
im_stdnonzero = empty_like(im_data)
data_stdnonzero = np.zeros_like(data_mean)
data_stdnonzero[mask_std_nonzero] = 1
im_stdnonzero.data = data_stdnonzero
im_stdnonzero.save(fname_stdnonzero, dtype=np.float32)
# Compute SNR in ROI
if fname_mask:
snr_roi = np.average(snr_map[mask_std_nonzero],
weights=mask[mask_std_nonzero])
elif method == 'diff':
# Check user selected exactly 2 volumes for this method.
if not len(index_vol) == 2:
raise ValueError(
f"Number of selected volumes: {len(index_vol)}. The method 'diff' should be used with "
f"exactly 2 volumes. You can specify the number of volumes with the flag '-vol'."
)
data_2vol = np.take(data, index_vol, axis=3)
# Compute mean across the two volumes
data_mean = np.mean(data_2vol, axis=3)
# Compute mean in ROI for each z-slice, if the slice in the mask is not null
mean_in_roi = [
np.average(data_mean[..., iz], weights=mask[..., iz])
for iz in range(nz) if np.any(mask[..., iz])
]
data_sub = np.subtract(data_2vol[:, :, :, 1], data_2vol[:, :, :, 0])
# Compute STD in the ROI for each z-slice. The "np.sqrt(2)" results from the variance of the subtraction of two
# distributions: var(A-B) = var(A) + var(B).
# More context in: https://github.com/spinalcordtoolbox/spinalcordtoolbox/issues/3481
std_in_roi = [
weighted_std(data_sub[..., iz] / np.sqrt(2), weights=mask[..., iz])
for iz in range(nz) if np.any(mask[..., iz])
]
# Compute SNR
snr_roi_slicewise = [m / s for m, s in zip(mean_in_roi, std_in_roi)]
snr_roi = sum(snr_roi_slicewise) / len(snr_roi_slicewise)
elif method == 'single':
# Check that the input volume is 3D, or if it is 4D, that the user selected exactly 1 volume for this method.
if dim == 3:
data3d = data
elif dim == 4:
if not len(index_vol) == 1:
raise ValueError(
f"Selected volumes: {index_vol}. The method 'single' should be used with "
f"exactly 1 volume. You can specify the index of the volume with the flag '-vol'."
)
data3d = np.squeeze(data[..., index_vol])
# Check that input noise mask is provided
if fname_mask_noise:
mask_noise = Image(fname_mask_noise).data
else:
raise parser.error(
"A noise mask is mandatory with '-method single'.")
# Check dimensionality of the noise mask
if len(mask_noise.shape) != 3:
raise ValueError(
f"Input noise mask dimension: {dim}. Input dimension for the noise mask should be 3."
)
# Check that non-null slices are consistent between mask and mask_noise.
for iz in range(nz):
if not np.any(mask[..., iz]) == np.any(mask_noise[..., iz]):
raise ValueError(
f"Slice {iz} is empty in either mask or mask_noise. Non-null slices should be "
f"consistent between mask and mask_noise.")
# Compute mean in ROI for each z-slice, if the slice in the mask is not null
mean_in_roi = [
np.average(data3d[..., iz], weights=mask[..., iz])
for iz in range(nz) if np.any(mask[..., iz])
]
std_in_roi = [
weighted_std(data3d[..., iz], weights=mask_noise[..., iz])
for iz in range(nz) if np.any(mask_noise[..., iz])
]
# Compute SNR
snr_roi_slicewise = [m / s for m, s in zip(mean_in_roi, std_in_roi)]
snr_roi = sum(snr_roi_slicewise) / len(snr_roi_slicewise)
if rayleigh_correction:
# Correcting for Rayleigh noise (see eq. A12 in Dietrich et al.)
snr_roi *= np.sqrt((4 - np.pi) / 2)
# Display result
if fname_mask:
printv('\nSNR_' + method + ' = ' + str(snr_roi) + '\n', type='info')
# Added function for text file
if file_name is not None:
with open(file_name, "w") as f:
f.write(str(snr_roi))
printv('\nFile saved to ' + file_name)
def concat_data(fname_in_list, dim, pixdim=None, squeeze_data=False):
"""
Concatenate data
:param im_in_list: list of Images or image filenames
:param dim: dimension: 0, 1, 2, 3.
:param pixdim: pixel resolution to join to image header
:param squeeze_data: bool: if True, remove the last dim if it is a singleton.
:return im_out: concatenated image
"""
# WARNING: calling concat_data in python instead of in command line causes a non understood issue (results are different with both options)
# from numpy import concatenate, expand_dims
dat_list = []
data_concat_list = []
# check if shape of first image is smaller than asked dim to concatenate along
# data0 = Image(fname_in_list[0]).data
# if len(data0.shape) <= dim:
# expand_dim = True
# else:
# expand_dim = False
for i, fname in enumerate(fname_in_list):
# if there is more than 100 images to concatenate, then it does it iteratively to avoid memory issue.
if i != 0 and i % 100 == 0:
data_concat_list.append(np.concatenate(dat_list, axis=dim))
im = Image(fname)
dat = im.data
# if image shape is smaller than asked dim, then expand dim
if len(dat.shape) <= dim:
dat = np.expand_dims(dat, dim)
dat_list = [dat]
del im
del dat
else:
im = Image(fname)
dat = im.data
# if image shape is smaller than asked dim, then expand dim
if len(dat.shape) <= dim:
dat = np.expand_dims(dat, dim)
dat_list.append(dat)
del im
del dat
if data_concat_list:
data_concat_list.append(np.concatenate(dat_list, axis=dim))
data_concat = np.concatenate(data_concat_list, axis=dim)
else:
data_concat = np.concatenate(dat_list, axis=dim)
# write file
im_out = msct_image.empty_like(Image(fname_in_list[0]))
im_out.data = data_concat
if isinstance(fname_in_list[0], str):
im_out.absolutepath = sct.add_suffix(fname_in_list[0], "_concat")
else:
if fname_in_list[0].absolutepath:
im_out.absolutepath = sct.add_suffix(fname_in_list[0].absolutepath, "_concat")
if pixdim is not None:
im_out.hdr['pixdim'] = pixdim
if squeeze_data and data_concat.shape[dim] == 1:
# remove the last dim if it is a singleton.
im_out.data = data_concat.reshape(tuple([ x for (idx_shape, x) in enumerate(data_concat.shape) if idx_shape != dim]))
else:
im_out.data = data_concat
return im_out
def concat_data(fname_in_list, dim, pixdim=None, squeeze_data=False):
"""
Concatenate data
:param im_in_list: list of Images or image filenames
:param dim: dimension: 0, 1, 2, 3.
:param pixdim: pixel resolution to join to image header
:param squeeze_data: bool: if True, remove the last dim if it is a singleton.
:return im_out: concatenated image
"""
# WARNING: calling concat_data in python instead of in command line causes a non understood issue (results are different with both options)
# from numpy import concatenate, expand_dims
dat_list = []
data_concat_list = []
# check if shape of first image is smaller than asked dim to concatenate along
# data0 = Image(fname_in_list[0]).data
# if len(data0.shape) <= dim:
# expand_dim = True
# else:
# expand_dim = False
for i, fname in enumerate(fname_in_list):
# if there is more than 100 images to concatenate, then it does it iteratively to avoid memory issue.
if i != 0 and i % 100 == 0:
data_concat_list.append(np.concatenate(dat_list, axis=dim))
im = Image(fname)
dat = im.data
# if image shape is smaller than asked dim, then expand dim
if len(dat.shape) <= dim:
dat = np.expand_dims(dat, dim)
dat_list = [dat]
del im
del dat
else:
im = Image(fname)
dat = im.data
# if image shape is smaller than asked dim, then expand dim
if len(dat.shape) <= dim:
dat = np.expand_dims(dat, dim)
dat_list.append(dat)
del im
del dat
if data_concat_list:
data_concat_list.append(np.concatenate(dat_list, axis=dim))
data_concat = np.concatenate(data_concat_list, axis=dim)
else:
data_concat = np.concatenate(dat_list, axis=dim)
# write file
im_out = msct_image.empty_like(Image(fname_in_list[0]))
im_out.data = data_concat
if isinstance(fname_in_list[0], str):
im_out.absolutepath = sct.add_suffix(fname_in_list[0], "_concat")
else:
if fname_in_list[0].absolutepath:
im_out.absolutepath = sct.add_suffix(fname_in_list[0].absolutepath,
"_concat")
if pixdim is not None:
im_out.hdr['pixdim'] = pixdim
if squeeze_data and data_concat.shape[dim] == 1:
# remove the last dim if it is a singleton.
im_out.data = data_concat.reshape(
tuple([
x for (idx_shape, x) in enumerate(data_concat.shape)
if idx_shape != dim
]))
else:
im_out.data = data_concat
return im_out
def main():
# Default params
param = Param()
# Get parser info
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
fname_data = arguments['-i']
if '-m' in arguments:
fname_mask = arguments['-m']
else:
fname_mask = ''
method = arguments["-method"]
if '-vol' in arguments:
index_vol_user = arguments['-vol']
else:
index_vol_user = ''
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
# Check parameters
if method == 'diff':
if not fname_mask:
sct.printv('You need to provide a mask with -method diff. Exit.', 1, type='error')
# Load data and orient to RPI
im_data = Image(fname_data).change_orientation('RPI')
data = im_data.data
if fname_mask:
mask = Image(fname_mask).change_orientation('RPI').data
# Retrieve selected volumes
if index_vol_user:
index_vol = parse_num_list(index_vol_user)
else:
index_vol = range(data.shape[3])
# Make sure user selected 2 volumes with diff method
if method == 'diff':
if not len(index_vol) == 2:
sct.printv('Method "diff" should be used with exactly two volumes (specify with flag "-vol").', 1, 'error')
# Compute SNR
# NB: "time" is assumed to be the 4th dimension of the variable "data"
if method == 'mult':
# Compute mean and STD across time
data_mean = np.mean(data[:, :, :, index_vol], axis=3)
data_std = np.std(data[:, :, :, index_vol], axis=3, ddof=1)
# Generate mask where std is different from 0
mask_std_nonzero = np.where(data_std > param.almost_zero)
snr_map = np.zeros_like(data_mean)
snr_map[mask_std_nonzero] = data_mean[mask_std_nonzero] / data_std[mask_std_nonzero]
# Output SNR map
fname_snr = sct.add_suffix(fname_data, '_SNR-' + method)
im_snr = empty_like(im_data)
im_snr.data = snr_map
im_snr.save(fname_snr, dtype=np.float32)
# Output non-zero mask
fname_stdnonzero = sct.add_suffix(fname_data, '_mask-STD-nonzero' + method)
im_stdnonzero = empty_like(im_data)
data_stdnonzero = np.zeros_like(data_mean)
data_stdnonzero[mask_std_nonzero] = 1
im_stdnonzero.data = data_stdnonzero
im_stdnonzero.save(fname_stdnonzero, dtype=np.float32)
# Compute SNR in ROI
if fname_mask:
mean_in_roi = np.average(data_mean[mask_std_nonzero], weights=mask[mask_std_nonzero])
std_in_roi = np.average(data_std[mask_std_nonzero], weights=mask[mask_std_nonzero])
snr_roi = mean_in_roi / std_in_roi
# snr_roi = np.average(snr_map[mask_std_nonzero], weights=mask[mask_std_nonzero])
elif method == 'diff':
data_2vol = np.take(data, index_vol, axis=3)
# Compute mean in ROI
data_mean = np.mean(data_2vol, axis=3)
mean_in_roi = np.average(data_mean, weights=mask)
data_sub = np.subtract(data_2vol[:, :, :, 1], data_2vol[:, :, :, 0])
_, std_in_roi = weighted_avg_and_std(data_sub, mask)
# Compute SNR, correcting for Rayleigh noise (see eq. 7 in Dietrich et al.)
snr_roi = (2/np.sqrt(2)) * mean_in_roi / std_in_roi
# Display result
if fname_mask:
sct.printv('\nSNR_' + method + ' = ' + str(snr_roi) + '\n', type='info')
