def execute(self):
"""
This method executes the symmetry detection
:return: returns the symmetry data
"""
img = Image(self.input_image)
raw_orientation = img.change_orientation()
data = np.squeeze(img.data)
dim = data.shape
section_length = dim[1]/self.nb_sections
result = np.zeros(dim)
for i in range(0, self.nb_sections):
if (i+1)*section_length > dim[1]:
y_length = (i+1)*section_length - ((i+1)*section_length - dim[1])
result[:, i*section_length:i*section_length + y_length, :] = symmetry_detector_right_left(data[:, i*section_length:i*section_length + y_length, :], cropped_xy=self.crop_xy)
sym = symmetry_detector_right_left(data[:, i*section_length:(i+1)*section_length, :], cropped_xy=self.crop_xy)
result[:, i*section_length:(i+1)*section_length, :] = sym
result_image = Image(img)
if len(result_image.data) == 4:
result_image.data = result[:,:,:,np.newaxis]
else:
result_image.data = result
result_image.change_orientation(raw_orientation)
return result_image.data
def compute_dti(fname_in, fname_bvals, fname_bvecs, prefix):
"""
Compute DTI.
:param fname_in: input 4d file.
:param bvals: bvals txt file
:param bvecs: bvecs txt file
:param prefix: output prefix. Example: "dti_"
:return: True/False
"""
# Open file.
from msct_image import Image
nii = Image(fname_in)
data = nii.data
print('data.shape (%d, %d, %d, %d)' % data.shape)
# open bvecs/bvals
from dipy.io import read_bvals_bvecs
bvals, bvecs = read_bvals_bvecs(fname_bvals, fname_bvecs)
from dipy.core.gradients import gradient_table
gtab = gradient_table(bvals, bvecs)
# # mask and crop the data. This is a quick way to avoid calculating Tensors on the background of the image.
# from dipy.segment.mask import median_otsu
# maskdata, mask = median_otsu(data, 3, 1, True, vol_idx=range(10, 50), dilate=2)
# print('maskdata.shape (%d, %d, %d, %d)' % maskdata.shape)
# fit tensor model
import dipy.reconst.dti as dti
tenmodel = dti.TensorModel(gtab)
tenfit = tenmodel.fit(data)
# Compute metrics
printv('Computing metrics...', param.verbose)
# FA
from dipy.reconst.dti import fractional_anisotropy
nii.data = fractional_anisotropy(tenfit.evals)
nii.setFileName(prefix + 'FA.nii.gz')
nii.save('float32')
# MD
from dipy.reconst.dti import mean_diffusivity
nii.data = mean_diffusivity(tenfit.evals)
nii.setFileName(prefix + 'MD.nii.gz')
nii.save('float32')
# RD
from dipy.reconst.dti import radial_diffusivity
nii.data = radial_diffusivity(tenfit.evals)
nii.setFileName(prefix + 'RD.nii.gz')
nii.save('float32')
# AD
from dipy.reconst.dti import axial_diffusivity
nii.data = axial_diffusivity(tenfit.evals)
nii.setFileName(prefix + 'AD.nii.gz')
nii.save('float32')
return True
def compute_dti(fname_in, fname_bvals, fname_bvecs, prefix):
"""
Compute DTI.
:param fname_in: input 4d file.
:param bvals: bvals txt file
:param bvecs: bvecs txt file
:param prefix: output prefix. Example: "dti_"
:return: True/False
"""
# Open file.
from msct_image import Image
nii = Image(fname_in)
data = nii.data
print('data.shape (%d, %d, %d, %d)' % data.shape)
# open bvecs/bvals
from dipy.io import read_bvals_bvecs
bvals, bvecs = read_bvals_bvecs(fname_bvals, fname_bvecs)
from dipy.core.gradients import gradient_table
gtab = gradient_table(bvals, bvecs)
# # mask and crop the data. This is a quick way to avoid calculating Tensors on the background of the image.
# from dipy.segment.mask import median_otsu
# maskdata, mask = median_otsu(data, 3, 1, True, vol_idx=range(10, 50), dilate=2)
# print('maskdata.shape (%d, %d, %d, %d)' % maskdata.shape)
# fit tensor model
import dipy.reconst.dti as dti
tenmodel = dti.TensorModel(gtab)
tenfit = tenmodel.fit(data)
# Compute metrics
printv('Computing metrics...', param.verbose)
# FA
from dipy.reconst.dti import fractional_anisotropy
nii.data = fractional_anisotropy(tenfit.evals)
nii.setFileName(prefix+'FA.nii.gz')
nii.save('float32')
# MD
from dipy.reconst.dti import mean_diffusivity
nii.data = mean_diffusivity(tenfit.evals)
nii.setFileName(prefix+'MD.nii.gz')
nii.save('float32')
# RD
from dipy.reconst.dti import radial_diffusivity
nii.data = radial_diffusivity(tenfit.evals)
nii.setFileName(prefix+'RD.nii.gz')
nii.save('float32')
# AD
from dipy.reconst.dti import axial_diffusivity
nii.data = axial_diffusivity(tenfit.evals)
nii.setFileName(prefix+'AD.nii.gz')
nii.save('float32')
return True
def main(args=None):
dim_list = ['x', 'y', 'z', 't']
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
fname_in = arguments["-i"]
fname_out = arguments["-o"]
verbose = int(arguments['-v'])
# Build fname_out
if fname_out == '':
path_in, file_in, ext_in = extract_fname(fname_in)
fname_out = path_in + file_in + '_mean' + ext_in
# Open file.
nii = Image(fname_in)
data = nii.data
# run command
if '-otsu' in arguments:
param = arguments['-otsu']
data_out = otsu(data, param)
elif '-otsu_adap' in arguments:
param = arguments['-otsu_adap']
data_out = otsu_adap(data, param[0], param[1])
elif '-otsu_median' in arguments:
param = arguments['-otsu_median']
data_out = otsu_median(data, param[0], param[1])
elif '-thr' in arguments:
param = arguments['-thr']
data_out = threshold(data, param)
elif '-percent' in arguments:
param = arguments['-percent']
data_out = perc(data, param)
elif '-mean' in arguments:
dim = dim_list.index(arguments['-mean'])
data_out = compute_mean(data, dim)
elif '-std' in arguments:
dim = dim_list.index(arguments['-std'])
data_out = compute_std(data, dim)
elif '-dilate' in arguments:
data_out = dilate(data, arguments['-dilate'])
elif '-erode' in arguments:
data_out = erode(data, arguments['-dilate'])
else:
printv('No process applied.', 1, 'warning')
return
# Write output
nii.data = data_out
nii.setFileName(fname_out)
nii.save()
# display message
printv('Created file:\n--> ' + fname_out + '\n', verbose, 'info')
def crop_from_mask_with_background(self):
from numpy import asarray, einsum
image_in = Image(self.input_filename)
data_array = asarray(image_in.data)
data_mask = 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 = einsum('ijk,ijk->ijk', data_mask, data_array)
elif dim == 2:
new_data = einsum('ij,ij->ij', 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 = einsum('ijk,ijk->ijk', data_mask_inv, data_background)
elif dim == 2:
data_background = einsum('ij,ij->ij', data_mask_inv, data_background)
new_data += data_background
# set image out
image_in.setFileName(self.output_filename)
image_in.data = new_data
image_in.save()
def crop_from_mask_with_background(self):
from numpy import asarray, einsum
image_in = Image(self.input_filename)
data_array = asarray(image_in.data)
data_mask = 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 = einsum('ijk,ijk->ijk', data_mask, data_array)
elif dim == 2:
new_data = einsum('ij,ij->ij', 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 = einsum('ijk,ijk->ijk', data_mask_inv,
data_background)
elif dim == 2:
data_background = einsum('ij,ij->ij', data_mask_inv,
data_background)
new_data += data_background
# set image out
image_in.setFileName(self.output_filename)
image_in.data = new_data
image_in.save()
def create_label_z(fname_seg, z, value):
"""
Create a label at coordinates x_center, y_center, z
:param fname_seg: segmentation
:param z: int
:return: fname_label
"""
fname_label = 'labelz.nii.gz'
nii = Image(fname_seg)
orientation_origin = nii.change_orientation(
'RPI') # change orientation to RPI
nx, ny, nz, nt, px, py, pz, pt = nii.dim # Get dimensions
# find x and y coordinates of the centerline at z using center of mass
from scipy.ndimage.measurements import center_of_mass
x, y = center_of_mass(nii.data[:, :, z])
x, y = int(round(x)), int(round(y))
nii.data[:, :, :] = 0
nii.data[x, y, z] = value
# dilate label to prevent it from disappearing due to nearestneighbor interpolation
from sct_maths import dilate
nii.data = dilate(nii.data, [3])
nii.setFileName(fname_label)
nii.change_orientation(
orientation_origin) # put back in original orientation
nii.save()
return fname_label
def main(args = None):
dim_list = ['x', 'y', 'z', 't']
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
fname_in = arguments["-i"]
fname_out = arguments["-o"]
verbose = int(arguments['-v'])
# Build fname_out
if fname_out == '':
path_in, file_in, ext_in = extract_fname(fname_in)
fname_out = path_in+file_in+'_mean'+ext_in
# Open file.
nii = Image(fname_in)
data = nii.data
# run command
if '-otsu' in arguments:
param = arguments['-otsu']
data_out = otsu(data, param)
elif '-otsu_adap' in arguments:
param = arguments['-otsu_adap']
data_out = otsu_adap(data, param[0], param[1])
elif '-otsu_median' in arguments:
param = arguments['-otsu_median']
data_out = otsu_median(data, param[0], param[1])
elif '-thr' in arguments:
param = arguments['-thr']
data_out = threshold(data, param)
elif '-percent' in arguments:
param = arguments['-percent']
data_out = perc(data, param)
elif '-mean' in arguments:
dim = dim_list.index(arguments['-mean'])
data_out = compute_mean(data, dim)
elif '-std' in arguments:
dim = dim_list.index(arguments['-std'])
data_out = compute_std(data, dim)
elif '-dilate' in arguments:
data_out = dilate(data, arguments['-dilate'])
elif '-erode' in arguments:
data_out = erode(data, arguments['-dilate'])
else:
printv('No process applied.', 1, 'warning')
return
# Write output
nii.data = data_out
nii.setFileName(fname_out)
nii.save()
# display message
printv('Created file:\n--> '+fname_out+'\n', verbose, 'info')
def concat_data(fname_in, fname_out, dim):
"""
Concatenate data
:param fname_in: list of file names.
:param fname_out:
:param dim: dimension: 0, 1, 2, 3.
:return: none
"""
# create empty list
list_data = []
# loop across files
for i in range(len(fname_in)):
# append data to list
list_data.append(Image(fname_in[i]).data)
# expand dimension of all elements in the list if necessary
if dim > list_data[0].ndim - 1:
list_data = [expand_dims(i, dim) for i in list_data]
# concatenate
try:
data_concat = concatenate(list_data, axis=dim)
except Exception as e:
sct.printv(
'\nERROR: Concatenation on line {}'.format(
sys.exc_info()[-1].tb_lineno) + '\n' + str(e) + '\n', 1,
'error')
# write file
im = Image(fname_in[0])
im.data = data_concat
im.setFileName(fname_out)
im.save()
def concat_data(fname_in, fname_out, dim):
"""
Concatenate data
:param fname_in: list of file names.
:param fname_out:
:param dim: dimension: 0, 1, 2, 3.
:return: none
"""
# create empty list
list_data = []
# loop across files
for i in range(len(fname_in)):
# append data to list
list_data.append(Image(fname_in[i]).data)
# expand dimension of all elements in the list if necessary
if dim > list_data[0].ndim-1:
list_data = [expand_dims(i, dim) for i in list_data]
# concatenate
try:
data_concat = concatenate(list_data, axis=dim)
except Exception as e:
sct.printv('\nERROR: Concatenation on line {}'.format(sys.exc_info()[-1].tb_lineno)+'\n'+str(e)+'\n', 1, 'error')
# write file
im = Image(fname_in[0])
im.data = data_concat
im.setFileName(fname_out)
im.save()
def validation(self):
name_ref_gm_seg = sct.extract_fname(self.ref_gm_seg)
im_ref_gm_seg = Image('../' + self.ref_gm_seg)
res_gm_seg_bin = Image('../' + self.res_names['gm_seg'])
res_wm_seg_bin = Image('../' + self.res_names['wm_seg'])
sct.run('cp ../' + self.ref_gm_seg + ' ./ref_gm_seg.nii.gz')
im_ref_wm_seg = inverse_gmseg_to_wmseg(im_ref_gm_seg, Image('../' + self.sc_seg_fname), 'ref_gm_seg')
im_ref_wm_seg.file_name = 'ref_wm_seg'
im_ref_wm_seg.ext = '.nii.gz'
im_ref_wm_seg.save()
if self.param.res_type == 'prob':
res_gm_seg_bin.data = np.asarray((res_gm_seg_bin.data >= 0.5).astype(int))
res_wm_seg_bin.data = np.asarray((res_wm_seg_bin.data >= 0.50001).astype(int))
res_gm_seg_bin.path = './'
res_gm_seg_bin.file_name = 'res_gm_seg_bin'
res_gm_seg_bin.ext = '.nii.gz'
res_gm_seg_bin.save()
res_wm_seg_bin.path = './'
res_wm_seg_bin.file_name = 'res_wm_seg_bin'
res_wm_seg_bin.ext = '.nii.gz'
res_wm_seg_bin.save()
try:
status_gm, output_gm = sct.run('sct_dice_coefficient ref_gm_seg.nii.gz res_gm_seg_bin.nii.gz -2d-slices 2', error_exit='warning', raise_exception=True)
except Exception:
sct.run('c3d res_gm_seg_bin.nii.gz ref_gm_seg.nii.gz -reslice-identity -o ref_in_res_space_gm.nii.gz ')
status_gm, output_gm = sct.run('sct_dice_coefficient ref_in_res_space_gm.nii.gz res_gm_seg_bin.nii.gz -2d-slices 2', error_exit='warning')
try:
status_wm, output_wm = sct.run('sct_dice_coefficient ref_wm_seg.nii.gz res_wm_seg_bin.nii.gz -2d-slices 2', error_exit='warning', raise_exception=True)
except Exception:
sct.run('c3d res_wm_seg_bin.nii.gz ref_wm_seg.nii.gz -reslice-identity -o ref_in_res_space_wm.nii.gz ')
status_wm, output_wm = sct.run('sct_dice_coefficient ref_in_res_space_wm.nii.gz res_wm_seg_bin.nii.gz -2d-slices 2', error_exit='warning')
dice_name = 'dice_' + self.param.res_type + '.txt'
dice_fic = open('../' + dice_name, 'w')
if self.param.res_type == 'prob':
dice_fic.write('WARNING : the probabilistic segmentations were binarized with a threshold at 0.5 to compute the dice coefficient \n')
dice_fic.write('\n--------------------------------------------------------------\nDice coefficient on the Gray Matter segmentation:\n')
dice_fic.write(output_gm)
dice_fic.write('\n\n--------------------------------------------------------------\nDice coefficient on the White Matter segmentation:\n')
dice_fic.write(output_wm)
dice_fic.close()
# sct.run(' mv ./' + dice_name + ' ../')
return dice_name
def get_minimum_path_nii(fname):
from msct_image import Image
data=Image(fname)
vesselness_data = data.data
raw_orient=data.change_orientation()
data.data=get_minimum_path(data.data, invert=1)
data.change_orientation(raw_orient)
data.file_name += '_minimalpath'
data.save()
def concat_data(fname_in_list, dim, pixdim=None):
"""
Concatenate data
:param im_in_list: list of images.
:param dim: dimension: 0, 1, 2, 3.
:param pixdim: pixel resolution to join to image header
: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(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 = 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 = expand_dims(dat, dim)
dat_list.append(dat)
del im
del dat
if data_concat_list:
data_concat_list.append(concatenate(dat_list, axis=dim))
data_concat = concatenate(data_concat_list, axis=dim)
else:
data_concat = concatenate(dat_list, axis=dim)
# write file
im_out = Image(fname_in_list[0]).copy()
im_out.data = data_concat
im_out.setFileName(im_out.file_name + '_concat' + im_out.ext)
if pixdim is not None:
im_out.hdr['pixdim'] = pixdim
return im_out
def get_minimum_path_nii(fname):
from msct_image import Image
data = Image(fname)
vesselness_data = data.data
raw_orient = data.change_orientation()
result, J1, J2 = get_minimum_path(data.data, invert=1)
data.data = result
data.change_orientation(raw_orient)
data.file_name += '_minimalpath'
data.save()
def concat_data(fname_in_list, dim, pixdim=None):
"""
Concatenate data
:param im_in_list: list of images.
:param dim: dimension: 0, 1, 2, 3.
:param pixdim: pixel resolution to join to image header
: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, squeeze
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(concatenate(dat_list, axis=dim))
im = Image(fname)
dat = im.data
if expand_dim:
dat = expand_dims(dat, dim)
dat_list = [dat]
del im
del dat
else:
im = Image(fname)
dat = im.data
if expand_dim:
dat = expand_dims(dat, dim)
dat_list.append(dat)
del im
del dat
if data_concat_list:
data_concat_list.append(concatenate(dat_list, axis=dim))
data_concat = concatenate(data_concat_list, axis=dim)
else:
data_concat = concatenate(dat_list, axis=dim)
# write file
im_out = Image(fname_in_list[0]).copy()
im_out.data = data_concat
im_out.setFileName(im_out.file_name+'_concat'+im_out.ext)
if pixdim is not None:
im_out.hdr['pixdim'] = pixdim
return im_out
def copy_header(fname_src, fname_dest):
"""
Copy header
:param fname_src: source file name
:param fname_dest: destination file name
:return:
"""
nii_src = Image(fname_src)
data_dest = Image(fname_dest).data
nii_src.setFileName(fname_dest)
nii_src.data = data_dest
nii_src.save()
def copy_header(fname_src, fname_dest):
"""
Copy header
:param fname_src: source file name
:param fname_dest: destination file name
:return:
"""
nii_src = Image(fname_src)
data_dest = Image(fname_dest).data
nii_src.setFileName(fname_dest)
nii_src.data = data_dest
nii_src.save()
def execute(self):
"""
This method executes the symmetry detection
:return: returns the symmetry data
"""
img = Image(self.input_image)
raw_orientation = img.change_orientation()
data = np.squeeze(img.data)
dim = data.shape
section_length = dim[1] / self.nb_sections
result = np.zeros(dim)
for i in range(0, self.nb_sections):
if (i + 1) * section_length > dim[1]:
y_length = (i + 1) * section_length - (
(i + 1) * section_length - dim[1])
result[:, i * section_length:i * section_length +
y_length, :] = symmetry_detector_right_left(
data[:, i * section_length:i * section_length +
y_length, :],
cropped_xy=self.crop_xy)
sym = symmetry_detector_right_left(
data[:, i * section_length:(i + 1) * section_length, :],
cropped_xy=self.crop_xy)
result[:, i * section_length:(i + 1) * section_length, :] = sym
result_image = Image(img)
if len(result_image.data) == 4:
result_image.data = result[:, :, :, np.newaxis]
else:
result_image.data = result
result_image.change_orientation(raw_orientation)
return result_image.data
def output_debug_file(self, img, data, file_name):
"""
This method writes a nifti file that corresponds to a step in the algorithm for easy debug.
The new nifti file uses the header from the the image passed as parameter
:param data: data to be written to file
:param file_name: filename...
:return: None
"""
if self.verbose == 2:
current_folder = os.getcwd()
# os.chdir(self.path_tmp)
try:
img = Image(img)
img.data = data
img.change_orientation(self.raw_orientation)
img.file_name = file_name
img.save()
except Exception, e:
print e
def post_processing_volume_wise(fname_in):
"""Post processing function."""
im_in = Image(fname_in)
data_in = im_in.data.astype(np.int)
data_in = _remove_blobs(data_in)
zz_zeros = [
zz for zz in range(im_in.dim[2])
if 1 not in list(np.unique(data_in[:, :, zz]))
]
zz_holes = _remove_extrem_holes(zz_zeros, im_in.dim[2] - 1, 0)
# filling z_holes, i.e. interpolate for z_slice not segmented
im_in.data = _fill_z_holes(zz_holes, data_in,
im_in.dim[6]) if len(zz_holes) else data_in
im_in.setFileName(fname_in)
im_in.save()
del im_in
def output_debug_file(self, img, data, file_name):
"""
This method writes a nifti file that corresponds to a step in the algorithm for easy debug.
The new nifti file uses the header from the the image passed as parameter
:param data: data to be written to file
:param file_name: filename...
:return: None
"""
if self.produce_output:
current_folder = os.getcwd()
os.chdir(self.debug_folder)
try:
img = Image(img)
img.data = data
img.change_orientation(self.raw_orientation)
img.file_name = file_name
img.save()
except Exception, e:
print e
os.chdir(current_folder)
def label_discs(fname_seg_labeled, verbose=1):
"""
Label discs from labaled_segmentation
:param fname_seg_labeld: fname of the labeled segmentation
:param verbose:
:return:
"""
# open labeled segmentation
im_seg_labeled = Image(fname_seg_labeled)
orientation_native = im_seg_labeled.change_orientation('RPI')
nx, ny, nz = im_seg_labeled.dim[0], im_seg_labeled.dim[
1], im_seg_labeled.dim[2]
data_disc = np.zeros([nx, ny, nz])
vertebral_level_previous = np.max(im_seg_labeled.data)
# loop across z
for iz in range(nz):
# get 2d slice
slice = im_seg_labeled.data[:, :, iz]
# check if at least one voxel is non-zero
if np.any(slice):
slice_one = np.copy(slice)
# set all non-zero values to 1
slice_one[slice.nonzero()] = 1
# compute center of mass
cx, cy = [
int(x) for x in np.round(center_of_mass(slice_one)).tolist()
]
# retrieve vertebral level
vertebral_level = slice[cx, cy]
# if smaller than previous level, then labeled as a disc
if vertebral_level < vertebral_level_previous:
# label disc
# sct.printv('iz='+iz+', disc='+vertebral_level)
data_disc[cx, cy, iz] = vertebral_level
# update variable
vertebral_level_previous = vertebral_level
# save disc labeled file
im_seg_labeled.file_name += '_disc'
im_seg_labeled.data = data_disc
im_seg_labeled.change_orientation(orientation_native)
im_seg_labeled.save()
def label_discs(fname_seg_labeled, verbose=1):
"""
Label discs from labaled_segmentation
:param fname_seg_labeld: fname of the labeled segmentation
:param verbose:
:return:
"""
# open labeled segmentation
im_seg_labeled = Image(fname_seg_labeled)
orientation_native = im_seg_labeled.change_orientation('RPI')
nx, ny, nz = im_seg_labeled.dim[0], im_seg_labeled.dim[1], im_seg_labeled.dim[2]
data_disc = np.zeros([nx, ny, nz])
vertebral_level_previous = np.max(im_seg_labeled.data)
# loop across z
for iz in range(nz):
# get 2d slice
slice = im_seg_labeled.data[:, :, iz]
# check if at least one voxel is non-zero
if np.any(slice):
slice_one = np.copy(slice)
# set all non-zero values to 1
slice_one[slice.nonzero()] = 1
# compute center of mass
cx, cy = [int(x) for x in np.round(center_of_mass(slice_one)).tolist()]
# retrieve vertebral level
vertebral_level = slice[cx, cy]
# if smaller than previous level, then labeled as a disc
if vertebral_level < vertebral_level_previous:
# label disc
# print 'iz='+iz+', disc='+vertebral_level
data_disc[cx, cy, iz] = vertebral_level
# update variable
vertebral_level_previous = vertebral_level
# save disc labeled file
im_seg_labeled.file_name += '_disc'
im_seg_labeled.data = data_disc
im_seg_labeled.change_orientation(orientation_native)
im_seg_labeled.save()
def create_label_z(fname_seg, z, value):
"""
Create a label at coordinates x_center, y_center, z
:param fname_seg: segmentation
:param z: int
:return: fname_label
"""
fname_label = 'labelz.nii.gz'
nii = Image(fname_seg)
orientation_origin = nii.change_orientation('RPI') # change orientation to RPI
nx, ny, nz, nt, px, py, pz, pt = nii.dim # Get dimensions
# find x and y coordinates of the centerline at z using center of mass
from scipy.ndimage.measurements import center_of_mass
x, y = center_of_mass(nii.data[:, :, z])
x, y = int(round(x)), int(round(y))
nii.data[:, :, :] = 0
nii.data[x, y, z] = value
# dilate label to prevent it from disappearing due to nearestneighbor interpolation
from sct_maths import dilate
nii.data = dilate(nii.data, [3])
nii.setFileName(fname_label)
nii.change_orientation(orientation_origin) # put back in original orientation
nii.save()
return fname_label
def main():
# Initialization
fname_data = ''
suffix_out = '_crop'
remove_temp_files = param.remove_temp_files
verbose = param.verbose
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
remove_temp_files = param.remove_temp_files
# Parameters for debug mode
if param.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
fname_data = path_sct+'/testing/data/errsm_23/t2/t2.nii.gz'
remove_temp_files = 0
else:
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:],'hi:r:v:')
except getopt.GetoptError:
usage()
if not opts:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-i'):
fname_data = arg
elif opt in ('-r'):
remove_temp_files = int(arg)
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname_data == '':
usage()
# Check file existence
sct.printv('\nCheck file existence...', verbose)
sct.check_file_exist(fname_data, verbose)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_data).dim
sct.printv('.. '+str(nx)+' x '+str(ny)+' x '+str(nz), verbose)
# check if 4D data
if not nt == 1:
sct.printv('\nERROR in '+os.path.basename(__file__)+': Data should be 3D.\n', 1, 'error')
sys.exit(2)
# print arguments
print '\nCheck parameters:'
print ' data ................... '+fname_data
print
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
path_out, file_out, ext_out = '', file_data+suffix_out, ext_data
# create temporary folder
path_tmp = 'tmp.'+time.strftime("%y%m%d%H%M%S")+'/'
sct.run('mkdir '+path_tmp)
# copy files into tmp folder
sct.run('isct_c3d '+fname_data+' -o '+path_tmp+'data.nii')
# go to tmp folder
os.chdir(path_tmp)
# change orientation
sct.printv('\nChange orientation to RPI...', verbose)
set_orientation('data.nii', 'RPI', 'data_rpi.nii')
# get image of medial slab
sct.printv('\nGet image of medial slab...', verbose)
image_array = nibabel.load('data_rpi.nii').get_data()
nx, ny, nz = image_array.shape
scipy.misc.imsave('image.jpg', image_array[math.floor(nx/2), :, :])
# Display the image
sct.printv('\nDisplay image and get cropping region...', verbose)
fig = plt.figure()
# fig = plt.gcf()
# ax = plt.gca()
ax = fig.add_subplot(111)
img = mpimg.imread("image.jpg")
implot = ax.imshow(img.T)
implot.set_cmap('gray')
plt.gca().invert_yaxis()
# mouse callback
ax.set_title('Left click on the top and bottom of your cropping field.\n Right click to remove last point.\n Close window when your done.')
line, = ax.plot([], [], 'ro') # empty line
cropping_coordinates = LineBuilder(line)
plt.show()
# disconnect callback
# fig.canvas.mpl_disconnect(line)
# check if user clicked two times
if len(cropping_coordinates.xs) != 2:
sct.printv('\nERROR: You have to select two points. Exit program.\n', 1, 'error')
sys.exit(2)
# convert coordinates to integer
zcrop = [int(i) for i in cropping_coordinates.ys]
# sort coordinates
zcrop.sort()
# crop image
sct.printv('\nCrop image...', verbose)
nii = Image('data_rpi.nii')
data_crop = nii.data[:, :, zcrop[0]:zcrop[1]]
nii.data = data_crop
nii.setFileName('data_rpi_crop.nii')
nii.save()
# come back to parent folder
os.chdir('..')
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(path_tmp+'data_rpi_crop.nii', path_out+file_out+ext_out)
# Remove temporary files
if remove_temp_files == 1:
print('\nRemove temporary files...')
sct.run('rm -rf '+path_tmp)
# to view results
print '\nDone! To view results, type:'
print 'fslview '+path_out+file_out+ext_out+' &'
print
def create_mask():
fsloutput = "export FSLOUTPUTTYPE=NIFTI; " # for faster processing, all outputs are in NIFTI
# display usage if a mandatory argument is not provided
if param.fname_data == "" or param.method == "":
sct.printv("\nERROR: All mandatory arguments are not provided. See usage (add -h).\n", 1, "error")
# parse argument for method
method_list = param.method.replace(" ", "").split(",") # remove spaces and parse with comma
# method_list = param.method.split(',') # parse with comma
method_type = method_list[0]
# check existence of method type
if not method_type in param.method_list:
sct.printv(
"\nERROR in "
+ os.path.basename(__file__)
+ ': Method "'
+ method_type
+ '" is not recognized. See usage (add -h).\n',
1,
"error",
)
# check method val
if not method_type == "center":
method_val = method_list[1]
del method_list
# check existence of shape
if not param.shape in param.shape_list:
sct.printv(
"\nERROR in "
+ os.path.basename(__file__)
+ ': Shape "'
+ param.shape
+ '" is not recognized. See usage (add -h).\n',
1,
"error",
)
# check existence of input files
sct.printv("\ncheck existence of input files...", param.verbose)
sct.check_file_exist(param.fname_data, param.verbose)
if method_type == "centerline":
sct.check_file_exist(method_val, param.verbose)
# check if orientation is RPI
sct.printv("\nCheck if orientation is RPI...", param.verbose)
status, output = sct.run("sct_orientation -i " + param.fname_data)
if not output == "RPI":
sct.printv(
"\nERROR in "
+ os.path.basename(__file__)
+ ": Orientation of input image should be RPI. Use sct_orientation to put your image in RPI.\n",
1,
"error",
)
# display input parameters
sct.printv("\nInput parameters:", param.verbose)
sct.printv(" data .................." + param.fname_data, param.verbose)
sct.printv(" method ................" + method_type, 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 = param.file_prefix + file_data + ext_data
# fname_out = os.path.abspath(path_out+file_out+ext_out)
# create temporary folder
sct.printv("\nCreate temporary folder...", param.verbose)
path_tmp = sct.slash_at_the_end("tmp." + time.strftime("%y%m%d%H%M%S"), 1)
sct.run("mkdir " + path_tmp, param.verbose)
# Copying input data to tmp folder and convert to nii
# NB: cannot use c3d here because c3d cannot convert 4D data.
sct.printv("\nCopying input data to tmp folder and convert to nii...", param.verbose)
convert(param.fname_data, path_tmp + "data.nii")
# sct.run('cp '+param.fname_data+' '+path_tmp+'data'+ext_data, param.verbose)
if method_type == "centerline":
convert(method_val, path_tmp + "centerline.nii.gz")
# sct.run('isct_c3d '+method_val+' -o '+path_tmp+'/centerline.nii.gz')
# go to tmp folder
os.chdir(path_tmp)
# Get dimensions of data
sct.printv("\nGet dimensions of data...", param.verbose)
nx, ny, nz, nt, px, py, pz, pt = Image("data.nii").dim
sct.printv(" " + str(nx) + " x " + str(ny) + " x " + str(nz) + " x " + str(nt), 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 3D.",
param.verbose,
"warning",
)
# extract first volume to have 3d reference
nii = Image("data.nii")
data3d = nii.data[:, :, :, 0]
nii.data = data3d
nii.save()
if method_type == "coord":
# parse to get coordinate
coord = map(int, method_val.split("x"))
if method_type == "point":
# get file name
fname_point = method_val
# extract coordinate of point
sct.printv("\nExtract coordinate of point...", param.verbose)
status, output = sct.run("sct_label_utils -i " + fname_point + " -t display-voxel", param.verbose)
# parse to get coordinate
coord = output[output.find("Position=") + 10 : -17].split(",")
if method_type == "center":
# set coordinate at center of FOV
coord = round(float(nx) / 2), round(float(ny) / 2)
if method_type == "centerline":
# get name of centerline from user argument
fname_centerline = "centerline.nii.gz"
else:
# generate volume with line along Z at coordinates 'coord'
sct.printv("\nCreate line...", param.verbose)
fname_centerline = create_line("data.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
data_centerline = centerline.get_data() # get centerline
z_centerline = [iz for iz in range(0, nz, 1) if data_centerline[:, :, iz].any()]
nz = len(z_centerline)
# get center of mass of the centerline
cx = [0] * nz
cy = [0] * nz
for iz in range(0, nz, 1):
cx[iz], cy[iz] = ndimage.measurements.center_of_mass(numpy.array(data_centerline[:, :, z_centerline[iz]]))
# create 2d masks
file_mask = "data_mask"
for iz in range(nz):
center = numpy.array([cx[iz], cy[iz]])
mask2d = create_mask2d(center, param.shape, param.size, nx, ny)
# Write NIFTI volumes
img = nibabel.Nifti1Image(mask2d, None, hdr)
nibabel.save(img, (file_mask + str(iz) + ".nii"))
# merge along Z
# cmd = 'fslmerge -z mask '
cmd = "sct_concat_data -dim z -o mask.nii.gz -i "
for iz in range(nz):
cmd = cmd + file_mask + str(iz) + ".nii,"
# remove ',' at the end of the string
cmd = cmd[:-1]
status, output = sct.run(cmd, param.verbose)
# copy geometry
copy_header("data.nii", "mask.nii.gz")
# come back to parent folder
os.chdir("..")
# Generate output files
sct.printv("\nGenerate output files...", param.verbose)
sct.generate_output_file(path_tmp + "mask.nii.gz", param.fname_out)
# Remove temporary files
if param.remove_tmp_files == 1:
sct.printv("\nRemove temporary files...", param.verbose)
sct.run("rm -rf " + path_tmp, param.verbose)
# to view results
sct.printv("\nDone! To view results, type:", param.verbose)
sct.printv("fslview " + param.fname_data + " " + param.fname_out + " -l Red -t 0.5 &", param.verbose, "info")
print
def create_mask():
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
# 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)
# check if orientation is RPI
sct.printv('\nCheck if orientation is RPI...', param.verbose)
ori = get_orientation(param.fname_data, filename=True)
if not ori == 'RPI':
sct.printv('\nERROR in '+os.path.basename(__file__)+': Orientation of input image should be RPI. Use sct_image -setorient to put your image in RPI.\n', 1, 'error')
# 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 = param.file_prefix+file_data+ext_data
#fname_out = os.path.abspath(path_out+file_out+ext_out)
# create temporary folder
sct.printv('\nCreate temporary folder...', param.verbose)
path_tmp = sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
sct.run('mkdir '+path_tmp, param.verbose)
# Copying input data to tmp folder and convert to nii
sct.printv('\nCopying input data to tmp folder and convert to nii...', param.verbose)
convert(param.fname_data, path_tmp+'data.nii')
# sct.run('cp '+param.fname_data+' '+path_tmp+'data'+ext_data, param.verbose)
if method_type == 'centerline':
convert(method_val, path_tmp+'centerline.nii.gz')
# go to tmp folder
os.chdir(path_tmp)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = Image('data.nii').dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz)+ ' x ' + str(nt), 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 3D.', param.verbose, 'warning')
# extract first volume to have 3d reference
nii = Image('data.nii')
data3d = nii.data[:,:,:,0]
nii.data = data3d
nii.save()
if method_type == 'coord':
# parse to get coordinate
coord = map(int, method_val.split('x'))
if method_type == 'point':
# get file name
fname_point = method_val
# extract coordinate of point
sct.printv('\nExtract coordinate of point...', param.verbose)
status, output = sct.run('sct_label_utils -i '+fname_point+' -p display-voxel', param.verbose)
# parse to get coordinate
coord = output[output.find('Position=')+10:-17].split(',')
if method_type == 'center':
# set coordinate at center of FOV
coord = round(float(nx)/2), round(float(ny)/2)
if method_type == 'centerline':
# get name of centerline from user argument
fname_centerline = 'centerline.nii.gz'
else:
# generate volume with line along Z at coordinates 'coord'
sct.printv('\nCreate line...', param.verbose)
fname_centerline = create_line('data.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
data_centerline = centerline.get_data() # get centerline
z_centerline = [iz for iz in range(0, nz, 1) if data_centerline[:, :, iz].any()]
nz = len(z_centerline)
# get center of mass of the centerline
cx = [0] * nz
cy = [0] * nz
for iz in range(0, nz, 1):
cx[iz], cy[iz] = ndimage.measurements.center_of_mass(numpy.array(data_centerline[:, :, z_centerline[iz]]))
# create 2d masks
file_mask = 'data_mask'
for iz in range(nz):
center = numpy.array([cx[iz], cy[iz]])
mask2d = create_mask2d(center, param.shape, param.size, nx, ny, param.even)
# Write NIFTI volumes
img = nibabel.Nifti1Image(mask2d, None, hdr)
nibabel.save(img, (file_mask+str(iz)+'.nii'))
# merge along Z
# cmd = 'fslmerge -z mask '
im_list = []
for iz in range(nz):
im_list.append(Image(file_mask+str(iz)+'.nii'))
im_out = concat_data(im_list, 2)
im_out.setFileName('mask.nii.gz')
im_out.save()
# copy geometry
im_dat = Image('data.nii')
im_mask = Image('mask.nii.gz')
im_mask = copy_header(im_dat, im_mask)
im_mask.save()
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', param.verbose)
sct.generate_output_file(path_tmp+'mask.nii.gz', param.fname_out)
# Remove temporary files
if param.remove_tmp_files == 1:
sct.printv('\nRemove temporary files...', param.verbose)
sct.run('rm -rf '+path_tmp, param.verbose, error_exit='warning')
# to view results
sct.printv('\nDone! To view results, type:', param.verbose)
sct.printv('fslview '+param.fname_data+' '+param.fname_out+' -l Red -t 0.5 &', param.verbose, 'info')
print
def main():
# get default parameters
step1 = Paramreg(step='1', type='seg', algo='slicereg', metric='MeanSquares', iter='10')
step2 = Paramreg(step='2', type='im', algo='syn', metric='MI', iter='3')
# step1 = Paramreg()
paramreg = ParamregMultiStep([step1, step2])
# step1 = Paramreg_step(step='1', type='seg', algo='bsplinesyn', metric='MeanSquares', iter='10', shrink='1', smooth='0', gradStep='0.5')
# step2 = Paramreg_step(step='2', type='im', algo='syn', metric='MI', iter='10', shrink='1', smooth='0', gradStep='0.5')
# paramreg = ParamregMultiStep([step1, step2])
# Initialize the parser
parser = Parser(__file__)
parser.usage.set_description('Register anatomical image to the template.')
parser.add_option(name="-i",
type_value="file",
description="Anatomical image.",
mandatory=True,
example="anat.nii.gz")
parser.add_option(name="-s",
type_value="file",
description="Spinal cord segmentation.",
mandatory=True,
example="anat_seg.nii.gz")
parser.add_option(name="-l",
type_value="file",
description="Labels. See: http://sourceforge.net/p/spinalcordtoolbox/wiki/create_labels/",
mandatory=True,
default_value='',
example="anat_labels.nii.gz")
parser.add_option(name="-t",
type_value="folder",
description="Path to MNI-Poly-AMU template.",
mandatory=False,
default_value=param.path_template)
parser.add_option(name="-p",
type_value=[[':'], 'str'],
description="""Parameters for registration (see sct_register_multimodal). Default:\n--\nstep=1\ntype="""+paramreg.steps['1'].type+"""\nalgo="""+paramreg.steps['1'].algo+"""\nmetric="""+paramreg.steps['1'].metric+"""\npoly="""+paramreg.steps['1'].poly+"""\n--\nstep=2\ntype="""+paramreg.steps['2'].type+"""\nalgo="""+paramreg.steps['2'].algo+"""\nmetric="""+paramreg.steps['2'].metric+"""\niter="""+paramreg.steps['2'].iter+"""\nshrink="""+paramreg.steps['2'].shrink+"""\nsmooth="""+paramreg.steps['2'].smooth+"""\ngradStep="""+paramreg.steps['2'].gradStep+"""\n--""",
mandatory=False,
example="step=2,type=seg,algo=bsplinesyn,metric=MeanSquares,iter=5,shrink=2:step=3,type=im,algo=syn,metric=MI,iter=5,shrink=1,gradStep=0.3")
parser.add_option(name="-r",
type_value="multiple_choice",
description="""Remove temporary files.""",
mandatory=False,
default_value='1',
example=['0', '1'])
parser.add_option(name="-v",
type_value="multiple_choice",
description="""Verbose. 0: nothing. 1: basic. 2: extended.""",
mandatory=False,
default_value=param.verbose,
example=['0', '1', '2'])
if param.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
fname_data = '/Users/julien/data/temp/sct_example_data/t2/t2.nii.gz'
fname_landmarks = '/Users/julien/data/temp/sct_example_data/t2/labels.nii.gz'
fname_seg = '/Users/julien/data/temp/sct_example_data/t2/t2_seg.nii.gz'
path_template = param.path_template
remove_temp_files = 0
verbose = 2
# speed = 'superfast'
#param_reg = '2,BSplineSyN,0.6,MeanSquares'
else:
arguments = parser.parse(sys.argv[1:])
# get arguments
fname_data = arguments['-i']
fname_seg = arguments['-s']
fname_landmarks = arguments['-l']
path_template = arguments['-t']
remove_temp_files = int(arguments['-r'])
verbose = int(arguments['-v'])
if '-p' in arguments:
paramreg_user = arguments['-p']
# update registration parameters
for paramStep in paramreg_user:
paramreg.addStep(paramStep)
# initialize other parameters
file_template = param.file_template
file_template_label = param.file_template_label
file_template_seg = param.file_template_seg
output_type = param.output_type
zsubsample = param.zsubsample
# smoothing_sigma = param.smoothing_sigma
# start timer
start_time = time.time()
# get absolute path - TO DO: remove! NEVER USE ABSOLUTE PATH...
path_template = os.path.abspath(path_template)
# get fname of the template + template objects
fname_template = sct.slash_at_the_end(path_template, 1)+file_template
fname_template_label = sct.slash_at_the_end(path_template, 1)+file_template_label
fname_template_seg = sct.slash_at_the_end(path_template, 1)+file_template_seg
# check file existence
sct.printv('\nCheck template files...')
sct.check_file_exist(fname_template, verbose)
sct.check_file_exist(fname_template_label, verbose)
sct.check_file_exist(fname_template_seg, verbose)
# print 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('.. Output type: '+str(output_type), verbose)
sct.printv('.. Remove temp files: '+str(remove_temp_files), verbose)
sct.printv('\nParameters for registration:')
for pStep in range(1, len(paramreg.steps)+1):
sct.printv('Step #'+paramreg.steps[str(pStep)].step, verbose)
sct.printv('.. Type #'+paramreg.steps[str(pStep)].type, verbose)
sct.printv('.. Algorithm................ '+paramreg.steps[str(pStep)].algo, verbose)
sct.printv('.. Metric................... '+paramreg.steps[str(pStep)].metric, verbose)
sct.printv('.. Number of iterations..... '+paramreg.steps[str(pStep)].iter, verbose)
sct.printv('.. Shrink factor............ '+paramreg.steps[str(pStep)].shrink, verbose)
sct.printv('.. Smoothing factor......... '+paramreg.steps[str(pStep)].smooth, verbose)
sct.printv('.. Gradient step............ '+paramreg.steps[str(pStep)].gradStep, verbose)
sct.printv('.. Degree of polynomial..... '+paramreg.steps[str(pStep)].poly, verbose)
path_data, file_data, ext_data = sct.extract_fname(fname_data)
sct.printv('\nCheck input labels...')
# check if label image contains coherent labels
image_label = Image(fname_landmarks)
# -> all labels must be different
labels = image_label.getNonZeroCoordinates(sorting='value')
hasDifferentLabels = True
for lab in labels:
for otherlabel in labels:
if lab != otherlabel and lab.hasEqualValue(otherlabel):
hasDifferentLabels = False
break
if not hasDifferentLabels:
sct.printv('ERROR: Wrong landmarks input. All labels must be different.', verbose, 'error')
# all labels must be available in tempalte
image_label_template = Image(fname_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 correspondance in tempalte space. \nLabel max '
'provided: ' + str(labels[-1].value) + '\nLabel max from template: ' +
str(labels_template[-1].value), verbose, 'error')
# create temporary folder
sct.printv('\nCreate temporary folder...', verbose)
path_tmp = 'tmp.'+time.strftime("%y%m%d%H%M%S")
status, output = sct.run('mkdir '+path_tmp)
# copy files to temporary folder
sct.printv('\nCopy files...', verbose)
sct.run('isct_c3d '+fname_data+' -o '+path_tmp+'/data.nii')
sct.run('isct_c3d '+fname_landmarks+' -o '+path_tmp+'/landmarks.nii.gz')
sct.run('isct_c3d '+fname_seg+' -o '+path_tmp+'/segmentation.nii.gz')
sct.run('isct_c3d '+fname_template+' -o '+path_tmp+'/template.nii')
sct.run('isct_c3d '+fname_template_label+' -o '+path_tmp+'/template_labels.nii.gz')
sct.run('isct_c3d '+fname_template_seg+' -o '+path_tmp+'/template_seg.nii.gz')
# go to tmp folder
os.chdir(path_tmp)
# resample data to 1mm isotropic
sct.printv('\nResample data to 1mm isotropic...', verbose)
sct.run('isct_c3d data.nii -resample-mm 1.0x1.0x1.0mm -interpolation Linear -o datar.nii')
sct.run('isct_c3d segmentation.nii.gz -resample-mm 1.0x1.0x1.0mm -interpolation NearestNeighbor -o segmentationr.nii.gz')
# N.B. resampling of labels is more complicated, because they are single-point labels, therefore resampling with neighrest neighbour can make them disappear. Therefore a more clever approach is required.
resample_labels('landmarks.nii.gz', 'datar.nii', 'landmarksr.nii.gz')
# # TODO
# sct.run('sct_label_utils -i datar.nii -t create -x 124,186,19,2:129,98,23,8 -o landmarksr.nii.gz')
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
set_orientation('datar.nii', 'RPI', 'data_rpi.nii')
set_orientation('landmarksr.nii.gz', 'RPI', 'landmarks_rpi.nii.gz')
set_orientation('segmentationr.nii.gz', 'RPI', 'segmentation_rpi.nii.gz')
# # Change orientation of input images to RPI
# sct.printv('\nChange orientation of input images to RPI...', verbose)
# set_orientation('data.nii', 'RPI', 'data_rpi.nii')
# set_orientation('landmarks.nii.gz', 'RPI', 'landmarks_rpi.nii.gz')
# set_orientation('segmentation.nii.gz', 'RPI', 'segmentation_rpi.nii.gz')
# get landmarks in native space
# crop segmentation
# output: segmentation_rpi_crop.nii.gz
sct.run('sct_crop_image -i segmentation_rpi.nii.gz -o segmentation_rpi_crop.nii.gz -dim 2 -bzmax')
# straighten segmentation
sct.printv('\nStraighten the spinal cord using centerline/segmentation...', verbose)
sct.run('sct_straighten_spinalcord -i segmentation_rpi_crop.nii.gz -c segmentation_rpi_crop.nii.gz -r 0 -v '+str(verbose), verbose)
# re-define warping field using non-cropped space (to avoid issue #367)
sct.run('sct_concat_transfo -w warp_straight2curve.nii.gz -d data_rpi.nii -o warp_straight2curve.nii.gz')
# 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 -t remove -i template_labels.nii.gz -o template_label.nii.gz -r landmarks_rpi.nii.gz')
# Make sure landmarks are INT
sct.printv('\nConvert landmarks to INT...', verbose)
sct.run('isct_c3d template_label.nii.gz -type int -o template_label.nii.gz', verbose)
# Create a cross for the template labels - 5 mm
sct.printv('\nCreate a 5 mm cross for the template labels...', verbose)
sct.run('sct_label_utils -t cross -i template_label.nii.gz -o template_label_cross.nii.gz -c 5')
# Create a cross for the input labels and dilate for straightening preparation - 5 mm
sct.printv('\nCreate a 5mm cross for the input labels and dilate for straightening preparation...', verbose)
sct.run('sct_label_utils -t cross -i landmarks_rpi.nii.gz -o landmarks_rpi_cross3x3.nii.gz -c 5 -d')
# Apply straightening to labels
sct.printv('\nApply straightening to labels...', verbose)
sct.run('sct_apply_transfo -i landmarks_rpi_cross3x3.nii.gz -o landmarks_rpi_cross3x3_straight.nii.gz -d segmentation_rpi_crop_straight.nii.gz -w warp_curve2straight.nii.gz -x nn')
# Convert landmarks from FLOAT32 to INT
sct.printv('\nConvert landmarks from FLOAT32 to INT...', verbose)
sct.run('isct_c3d landmarks_rpi_cross3x3_straight.nii.gz -type int -o landmarks_rpi_cross3x3_straight.nii.gz')
# Remove labels that do not correspond with each others.
sct.printv('\nRemove labels that do not correspond with each others.', verbose)
sct.run('sct_label_utils -t remove-symm -i landmarks_rpi_cross3x3_straight.nii.gz -o landmarks_rpi_cross3x3_straight.nii.gz,template_label_cross.nii.gz -r template_label_cross.nii.gz')
# Estimate affine transfo: straight --> template (landmark-based)'
sct.printv('\nEstimate affine transfo: straight anat --> template (landmark-based)...', verbose)
# converting landmarks straight and curved to physical coordinates
image_straight = Image('landmarks_rpi_cross3x3_straight.nii.gz')
landmark_straight = image_straight.getNonZeroCoordinates(sorting='value')
image_template = Image('template_label_cross.nii.gz')
landmark_template = image_template.getNonZeroCoordinates(sorting='value')
# Reorganize landmarks
points_fixed, points_moving = [], []
landmark_straight_mean = []
for coord in landmark_straight:
if coord.value not in [c.value for c in landmark_straight_mean]:
temp_landmark = coord
temp_number = 1
for other_coord in landmark_straight:
if coord.hasEqualValue(other_coord) and coord != other_coord:
temp_landmark += other_coord
temp_number += 1
landmark_straight_mean.append(temp_landmark / temp_number)
for coord in landmark_straight_mean:
point_straight = image_straight.transfo_pix2phys([[coord.x, coord.y, coord.z]])
points_moving.append([point_straight[0][0], point_straight[0][1], point_straight[0][2]])
for coord in landmark_template:
point_template = image_template.transfo_pix2phys([[coord.x, coord.y, coord.z]])
points_fixed.append([point_template[0][0], point_template[0][1], point_template[0][2]])
# Register curved landmarks on straight landmarks based on python implementation
sct.printv('\nComputing rigid transformation (algo=translation-scaling-z) ...', verbose)
import msct_register_landmarks
(rotation_matrix, translation_array, points_moving_reg, points_moving_barycenter) = \
msct_register_landmarks.getRigidTransformFromLandmarks(
points_fixed, points_moving, constraints='translation-scaling-z', show=False)
# writing rigid transformation file
text_file = open("straight2templateAffine.txt", "w")
text_file.write("#Insight Transform File V1.0\n")
text_file.write("#Transform 0\n")
text_file.write("Transform: FixedCenterOfRotationAffineTransform_double_3_3\n")
text_file.write("Parameters: %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f\n" % (
1.0/rotation_matrix[0, 0], rotation_matrix[0, 1], rotation_matrix[0, 2],
rotation_matrix[1, 0], 1.0/rotation_matrix[1, 1], rotation_matrix[1, 2],
rotation_matrix[2, 0], rotation_matrix[2, 1], 1.0/rotation_matrix[2, 2],
translation_array[0, 0], translation_array[0, 1], -translation_array[0, 2]))
text_file.write("FixedParameters: %.9f %.9f %.9f\n" % (points_moving_barycenter[0],
points_moving_barycenter[1],
points_moving_barycenter[2]))
text_file.close()
# Apply affine transformation: straight --> template
sct.printv('\nApply affine transformation: straight --> template...', verbose)
sct.run('sct_concat_transfo -w warp_curve2straight.nii.gz,straight2templateAffine.txt -d template.nii -o warp_curve2straightAffine.nii.gz')
sct.run('sct_apply_transfo -i data_rpi.nii -o data_rpi_straight2templateAffine.nii -d template.nii -w warp_curve2straightAffine.nii.gz')
sct.run('sct_apply_transfo -i segmentation_rpi.nii.gz -o segmentation_rpi_straight2templateAffine.nii.gz -d template.nii -w warp_curve2straightAffine.nii.gz -x linear')
# threshold to 0.5
nii = Image('segmentation_rpi_straight2templateAffine.nii.gz')
data = nii.data
data[data < 0.5] = 0
nii.data = data
nii.setFileName('segmentation_rpi_straight2templateAffine_th.nii.gz')
nii.save()
# find min-max of anat2template (for subsequent cropping)
zmin_template, zmax_template = find_zmin_zmax('segmentation_rpi_straight2templateAffine_th.nii.gz')
# crop template in z-direction (for faster processing)
sct.printv('\nCrop data in template space (for faster processing)...', verbose)
sct.run('sct_crop_image -i template.nii -o template_crop.nii -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
sct.run('sct_crop_image -i template_seg.nii.gz -o template_seg_crop.nii.gz -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
sct.run('sct_crop_image -i data_rpi_straight2templateAffine.nii -o data_rpi_straight2templateAffine_crop.nii -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
sct.run('sct_crop_image -i segmentation_rpi_straight2templateAffine.nii.gz -o segmentation_rpi_straight2templateAffine_crop.nii.gz -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
# sub-sample in z-direction
sct.printv('\nSub-sample in z-direction (for faster processing)...', verbose)
sct.run('sct_resample -i template_crop.nii -o template_crop_r.nii -f 1x1x'+zsubsample, verbose)
sct.run('sct_resample -i template_seg_crop.nii.gz -o template_seg_crop_r.nii.gz -f 1x1x'+zsubsample, verbose)
sct.run('sct_resample -i data_rpi_straight2templateAffine_crop.nii -o data_rpi_straight2templateAffine_crop_r.nii -f 1x1x'+zsubsample, verbose)
sct.run('sct_resample -i segmentation_rpi_straight2templateAffine_crop.nii.gz -o segmentation_rpi_straight2templateAffine_crop_r.nii.gz -f 1x1x'+zsubsample, verbose)
# 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)+1):
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 = 'data_rpi_straight2templateAffine_crop_r.nii'
dest = 'template_crop_r.nii'
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = 'segmentation_rpi_straight2templateAffine_crop_r.nii.gz'
dest = 'template_seg_crop_r.nii.gz'
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# if step>1, apply warp_forward_concat to the src image to be used
if i_step > 1:
# sct.run('sct_apply_transfo -i '+src+' -d '+dest+' -w '+','.join(warp_forward)+' -o '+sct.add_suffix(src, '_reg')+' -x '+interp_step, verbose)
sct.run('sct_apply_transfo -i '+src+' -d '+dest+' -w '+','.join(warp_forward)+' -o '+sct.add_suffix(src, '_reg')+' -x '+interp_step, verbose)
src = sct.add_suffix(src, '_reg')
# register src --> dest
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)
warp_inverse.reverse()
sct.run('sct_concat_transfo -w '+','.join(warp_inverse)+',-straight2templateAffine.txt,warp_straight2curve.nii.gz -d data.nii -o warp_template2anat.nii.gz', verbose)
# Apply warping fields to anat and template
if output_type == 1:
sct.run('sct_apply_transfo -i template.nii -o template2anat.nii.gz -d data.nii -w warp_template2anat.nii.gz -c 1', verbose)
sct.run('sct_apply_transfo -i data.nii -o anat2template.nii.gz -d template.nii -w warp_anat2template.nii.gz -c 1', verbose)
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(path_tmp+'/warp_template2anat.nii.gz', 'warp_template2anat.nii.gz', verbose)
sct.generate_output_file(path_tmp+'/warp_anat2template.nii.gz', 'warp_anat2template.nii.gz', verbose)
if output_type == 1:
sct.generate_output_file(path_tmp+'/template2anat.nii.gz', 'template2anat'+ext_data, verbose)
sct.generate_output_file(path_tmp+'/anat2template.nii.gz', 'anat2template'+ext_data, verbose)
# Delete temporary files
if remove_temp_files:
sct.printv('\nDelete temporary files...', verbose)
sct.run('rm -rf '+path_tmp)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: '+str(int(round(elapsed_time)))+'s', verbose)
# to view results
sct.printv('\nTo view results, type:', verbose)
sct.printv('fslview '+fname_data+' template2anat -b 0,4000 &', verbose, 'info')
sct.printv('fslview '+fname_template+' -b 0,5000 anat2template &\n', verbose, 'info')
def main(args=None):
# Initialization
# fname_anat = ''
# fname_centerline = ''
sigma = 3 # default value of the standard deviation for the Gaussian smoothing (in terms of number of voxels)
# remove_temp_files = param.remove_temp_files
# verbose = param.verbose
start_time = time.time()
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
fname_anat = arguments['-i']
fname_centerline = arguments['-s']
if '-smooth' in arguments:
sigma = arguments['-smooth']
if '-r' in arguments:
remove_temp_files = int(arguments['-r'])
if '-v' in arguments:
verbose = int(arguments['-v'])
# Display arguments
print '\nCheck input arguments...'
print ' Volume to smooth .................. ' + fname_anat
print ' Centerline ........................ ' + fname_centerline
print ' Sigma (mm) ........................ '+str(sigma)
print ' Verbose ........................... '+str(verbose)
# Check that input is 3D:
from msct_image import Image
nx, ny, nz, nt, px, py, pz, pt = Image(fname_anat).dim
dim = 4 # by default, will be adjusted later
if nt == 1:
dim = 3
if nz == 1:
dim = 2
if dim == 4:
sct.printv('WARNING: the input image is 4D, please split your image to 3D before smoothing spinalcord using :\n'
'sct_image -i '+fname_anat+' -split t -o '+fname_anat, verbose, 'warning')
sct.printv('4D images not supported, aborting ...', verbose, 'error')
# Extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
path_centerline, file_centerline, ext_centerline = sct.extract_fname(fname_centerline)
# create temporary folder
sct.printv('\nCreate temporary folder...', verbose)
path_tmp = sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
sct.run('mkdir '+path_tmp, verbose)
# Copying input data to tmp folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
sct.run('cp '+fname_anat+' '+path_tmp+'anat'+ext_anat, verbose)
sct.run('cp '+fname_centerline+' '+path_tmp+'centerline'+ext_centerline, verbose)
# go to tmp folder
os.chdir(path_tmp)
# convert to nii format
convert('anat'+ext_anat, 'anat.nii')
convert('centerline'+ext_centerline, 'centerline.nii')
# Change orientation of the input image into RPI
print '\nOrient input volume to RPI orientation...'
fname_anat_rpi = set_orientation('anat.nii', 'RPI', filename=True)
move(fname_anat_rpi, 'anat_rpi.nii')
# Change orientation of the input image into RPI
print '\nOrient centerline to RPI orientation...'
fname_centerline_rpi = set_orientation('centerline.nii', 'RPI', filename=True)
move(fname_centerline_rpi, 'centerline_rpi.nii')
# Straighten the spinal cord
# 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)
if os.path.isfile('../warp_curve2straight.nii.gz') and os.path.isfile('../warp_straight2curve.nii.gz') and os.path.isfile('../straight_ref.nii.gz'):
# if they exist, copy them into current folder
sct.printv('WARNING: Straightening was already run previously. Copying warping fields...', verbose, 'warning')
shutil.copy('../warp_curve2straight.nii.gz', 'warp_curve2straight.nii.gz')
shutil.copy('../warp_straight2curve.nii.gz', 'warp_straight2curve.nii.gz')
shutil.copy('../straight_ref.nii.gz', 'straight_ref.nii.gz')
# apply straightening
sct.run('sct_apply_transfo -i anat_rpi.nii -w warp_curve2straight.nii.gz -d straight_ref.nii.gz -o anat_rpi_straight.nii -x spline', verbose)
else:
sct.run('sct_straighten_spinalcord -i anat_rpi.nii -s centerline_rpi.nii -qc 0 -x spline', verbose)
# Smooth the straightened image along z
print '\nSmooth the straightened image along z...'
sct.run('sct_maths -i anat_rpi_straight.nii -smooth 0,0,'+str(sigma)+' -o anat_rpi_straight_smooth.nii', verbose)
# Apply the reversed warping field to get back the curved spinal cord
print '\nApply the reversed warping field to get back the curved spinal cord...'
sct.run('sct_apply_transfo -i anat_rpi_straight_smooth.nii -o anat_rpi_straight_smooth_curved.nii -d anat.nii -w warp_straight2curve.nii.gz -x spline', verbose)
# replace zeroed voxels by original image (issue #937)
sct.printv('\nReplace zeroed voxels by original image...', verbose)
nii_smooth = Image('anat_rpi_straight_smooth_curved.nii')
data_smooth = nii_smooth.data
data_input = Image('anat.nii').data
indzero = np.where(data_smooth == 0)
data_smooth[indzero] = data_input[indzero]
nii_smooth.data = data_smooth
nii_smooth.setFileName('anat_rpi_straight_smooth_curved_nonzero.nii')
nii_smooth.save()
# come back to parent folder
os.chdir('..')
# Generate output file
print '\nGenerate output file...'
sct.generate_output_file(path_tmp+'/anat_rpi_straight_smooth_curved_nonzero.nii', file_anat+'_smooth'+ext_anat)
# Remove temporary files
if remove_temp_files == 1:
print('\nRemove temporary files...')
sct.run('rm -rf '+path_tmp)
# Display elapsed time
elapsed_time = time.time() - start_time
print '\nFinished! Elapsed time: '+str(int(round(elapsed_time)))+'s\n'
# to view results
sct.printv('Done! To view results, type:', verbose)
sct.printv('fslview '+file_anat+' '+file_anat+'_smooth &\n', verbose, 'info')
def main():
# create temporary folder
sct.printv('\nCreate temporary folder...', verbose, 'normal')
path_tmp = 'tmp.'+time.strftime("%y%m%d%H%M%S")+'/'
sct.run('mkdir '+path_tmp)
target = 'target.nii.gz'
target_seg = 'target_seg.nii.gz'
anat = 'anat.nii.gz'
anat_seg = 'anat_seg.nii.gz'
warp_template2anat = 'warp_template2anat.nii.gz'
sct.run('cp '+multimodal_reg_param.anat+' '+path_tmp+anat)
sct.run('cp '+multimodal_reg_param.target+' '+path_tmp+target)
sct.run('cp '+multimodal_reg_param.anat_seg+' '+path_tmp+anat_seg)
sct.run('cp '+multimodal_reg_param.target_seg+' '+path_tmp+target_seg)
sct.run('cp '+multimodal_reg_param.warp_template2anat+' '+path_tmp+warp_template2anat)
os.chdir(path_tmp)
# registration of the template to the target
warp_anat2target, warp_target2anat, label_original = reg_multimodal_warp(anat, target, anat_seg, target_seg, warp_template2anat, multimodal_reg_param)
# segmentation of the gray matter
gm_seg_param.res_type = 'binary'
wm_fname, gm_fname = segment_gm(target_fname=target, sc_seg_fname=target_seg, path_to_label=label_original, param=gm_seg_param)
wm_seg_out = sct.extract_fname(multimodal_reg_param.target)[1]+'_wm_seg.nii.gz'
gm_seg_out = sct.extract_fname(multimodal_reg_param.target)[1]+'_gm_seg.nii.gz'
sct.run('cp '+wm_fname+' ../'+wm_seg_out)
sct.run('cp '+gm_fname+' ../'+gm_seg_out)
# registration of the template WM to the automatic Wm segmentation
wm_reg_param.fname_fixed = wm_fname
wm_reg_param.fname_moving = label_original + '/template/MNI-Poly-AMU_WM.nii.gz'
wm_reg_param.fname_seg_fixed = target_seg
wm_reg_param.fname_seg_moving = label_original + '/template/MNI-Poly-AMU_cord.nii.gz'
if gm_seg_param.res_type == 'binary':
# binarize template WM
template_wm = Image(wm_reg_param.fname_moving)
template_wm.data = (template_wm.data > 0.5).astype(int)
template_wm.save()
warp, inverse_warp = wm_registration(wm_reg_param)
warp_anat2target_corrected = 'warp_'+sct.extract_fname(multimodal_reg_param.anat)[1]+'2'+sct.extract_fname(multimodal_reg_param.target)[1]+'_corrected_wm.nii.gz'
warp_target2anat_corrected = 'warp_'+sct.extract_fname(multimodal_reg_param.target)[1]+'2'+sct.extract_fname(multimodal_reg_param.anat)[1]+'_corrected_wm.nii.gz'
sct.run('sct_concat_transfo -w '+warp_anat2target+','+warp+' -d '+target+' -o '+warp_anat2target_corrected)
sct.run('sct_concat_transfo -w '+inverse_warp+','+warp_target2anat+' -d '+anat+' -o '+warp_target2anat_corrected)
target_reg = sct.extract_fname(multimodal_reg_param.target)[1]+'_reg_corrected.nii.gz'
anat_reg = sct.extract_fname(multimodal_reg_param.anat)[1]+'_reg_corrected.nii.gz'
sct.run('sct_apply_transfo -i '+target+' -d '+anat+' -w '+warp_target2anat_corrected+' -o '+target_reg)
sct.run('sct_apply_transfo -i '+anat+' -d '+target+' -w '+warp_anat2target_corrected+' -o '+anat_reg)
sct.run('cp '+target_reg+' ../'+target_reg)
sct.run('cp '+anat_reg+' ../'+anat_reg)
sct.run('cp '+warp_anat2target_corrected+' ../'+warp_anat2target_corrected)
sct.run('cp '+warp_target2anat_corrected+' ../'+warp_target2anat_corrected)
sct.printv('Done!\n'
'You can warp the template to the target using the following command lines:\n'
'sct_concat_transfo -w '+multimodal_reg_param.warp_template2anat+','+warp_anat2target_corrected+' -d '+multimodal_reg_param.target+' -o warp_template2'+sct.extract_fname(multimodal_reg_param.target)[1]+'.nii.gz\n'
'sct_warp_template -d '+multimodal_reg_param.target+' -w warp_template2'+sct.extract_fname(multimodal_reg_param.target)[1]+'.nii.gz', verbose, 'info')
os.chdir('..')
if remove:
sct.printv('\nRemove temporary files...', verbose, 'normal')
sct.run("rm -rf "+path_tmp)
def crop_with_gui(self):
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
# Initialization
fname_data = self.input_filename
suffix_out = '_crop'
remove_temp_files = self.rm_tmp_files
verbose = self.verbose
# Check file existence
sct.printv('\nCheck file existence...', verbose)
sct.check_file_exist(fname_data, verbose)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_data).dim
sct.printv('.. ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz),
verbose)
# check if 4D data
if not nt == 1:
sct.printv(
'\nERROR in ' + os.path.basename(__file__) +
': Data should be 3D.\n', 1, 'error')
sys.exit(2)
# sct.printv(arguments)
sct.printv('\nCheck parameters:')
sct.printv(' data ................... ' + fname_data)
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
path_out, file_out, ext_out = '', file_data + suffix_out, ext_data
# create temporary folder
path_tmp = 'tmp.' + time.strftime("%y%m%d%H%M%S") + '/'
sct.run('mkdir ' + path_tmp)
# copy files into tmp folder
from sct_convert import convert
sct.printv('\nCopying input data to tmp folder and convert to nii...',
verbose)
convert(fname_data, path_tmp + 'data.nii')
# go to tmp folder
os.chdir(path_tmp)
# change orientation
sct.printv('\nChange orientation to RPI...', verbose)
sct.run('sct_image -i data.nii -setorient RPI -o data_rpi.nii')
# get image of medial slab
sct.printv('\nGet image of medial slab...', verbose)
image_array = nibabel.load('data_rpi.nii').get_data()
nx, ny, nz = image_array.shape
scipy.misc.imsave('image.jpg', image_array[math.floor(nx / 2), :, :])
# Display the image
sct.printv('\nDisplay image and get cropping region...', verbose)
fig = plt.figure()
# fig = plt.gcf()
# ax = plt.gca()
ax = fig.add_subplot(111)
img = mpimg.imread("image.jpg")
implot = ax.imshow(img.T)
implot.set_cmap('gray')
plt.gca().invert_yaxis()
# mouse callback
ax.set_title(
'Left click on the top and bottom of your cropping field.\n Right click to remove last point.\n Close window when your done.'
)
line, = ax.plot([], [], 'ro') # empty line
cropping_coordinates = LineBuilder(line)
plt.show()
# disconnect callback
# fig.canvas.mpl_disconnect(line)
# check if user clicked two times
if len(cropping_coordinates.xs) != 2:
sct.printv(
'\nERROR: You have to select two points. Exit program.\n', 1,
'error')
sys.exit(2)
# convert coordinates to integer
zcrop = [int(i) for i in cropping_coordinates.ys]
# sort coordinates
zcrop.sort()
# crop image
sct.printv('\nCrop image...', verbose)
nii = Image('data_rpi.nii')
data_crop = nii.data[:, :, zcrop[0]:zcrop[1]]
nii.data = data_crop
nii.setFileName('data_rpi_crop.nii')
nii.save()
# come back to parent folder
os.chdir('..')
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(path_tmp + 'data_rpi_crop.nii',
path_out + file_out + ext_out)
# Remove temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...')
sct.run('rm -rf ' + path_tmp)
# to view results
sct.printv('\nDone! To view results, type:')
sct.printv('fslview ' + path_out + file_out + ext_out + ' &')
sct.printv()
def process(self):
# preprocessing
os.chdir(self.tmp_dir)
im_target = Image(self.original_target)
im_sc_seg = Image(self.original_sc_seg)
self.original_header = im_target.hdr
self.original_orientation = im_target.orientation
index_x = self.original_orientation.find('R') if 'R' in self.original_orientation else self.original_orientation.find('L')
index_y = self.original_orientation.find('P') if 'P' in self.original_orientation else self.original_orientation.find('A')
index_z = self.original_orientation.find('I') if 'I' in self.original_orientation else self.original_orientation.find('S')
# resampling of the images
nx, ny, nz, nt, px, py, pz, pt = im_target.dim
pix_dim = [px, py, pz]
self.original_px = pix_dim[index_x]
self.original_py = pix_dim[index_y]
if round(self.original_px, 2) != self.resample_to or round(self.original_py, 2) != self.resample_to:
self.t2star = resample_image(self.original_target, npx=self.resample_to, npy=self.resample_to)
self.sc_seg = resample_image(self.original_sc_seg, binary=True, npx=self.resample_to, npy=self.resample_to)
# denoising (optional)
im_target = Image(self.t2star)
if self.denoising:
from sct_maths import denoise_ornlm
im_target.data = denoise_ornlm(im_target.data)
im_target.save()
self.t2star = im_target.file_name + im_target.ext
box_size = int(22.5/self.resample_to)
# Pad in case the spinal cord is too close to the edges
pad_size = box_size/2 + 2
self.pad = [str(pad_size)]*3
self.pad[index_z] = str(0)
t2star_pad = sct.add_suffix(self.t2star, '_pad')
sc_seg_pad = sct.add_suffix(self.sc_seg, '_pad')
sct.run('sct_image -i '+self.t2star+' -pad '+self.pad[0]+','+self.pad[1]+','+self.pad[2]+' -o '+t2star_pad)
sct.run('sct_image -i '+self.sc_seg+' -pad '+self.pad[0]+','+self.pad[1]+','+self.pad[2]+' -o '+sc_seg_pad)
self.t2star = t2star_pad
self.sc_seg = sc_seg_pad
# put data in RPI
t2star_rpi = sct.add_suffix(self.t2star, '_RPI')
sc_seg_rpi = sct.add_suffix(self.sc_seg, '_RPI')
sct.run('sct_image -i '+self.t2star+' -setorient RPI -o '+t2star_rpi)
sct.run('sct_image -i '+self.sc_seg+' -setorient RPI -o '+sc_seg_rpi)
self.t2star = t2star_rpi
self.sc_seg = sc_seg_rpi
self.square_mask, self.processed_target = crop_t2_star(self.t2star, self.sc_seg, box_size=box_size)
if self.t2 is not None:
self.fname_level = compute_level_file(self.t2star, self.sc_seg, self.t2, self.t2_seg, self.t2_landmarks)
elif self.fname_level is not None:
level_orientation = get_orientation(self.fname_level, filename=True)
if level_orientation != 'IRP':
self.fname_level = set_orientation(self.fname_level, 'IRP', filename=True)
os.chdir('..')
def get_centerline_from_point(input_image, point_file, gap=4, gaussian_kernel=4, remove_tmp_files=1):
# Initialization
fname_anat = input_image
fname_point = point_file
slice_gap = gap
remove_tmp_files = remove_tmp_files
gaussian_kernel = gaussian_kernel
start_time = time()
verbose = 1
# get path of the toolbox
status, path_sct = commands.getstatusoutput("echo $SCT_DIR")
path_sct = sct.slash_at_the_end(path_sct, 1)
# Parameters for debug mode
if param.debug == 1:
sct.printv("\n*** WARNING: DEBUG MODE ON ***\n\t\t\tCurrent working directory: " + os.getcwd(), "warning")
status, path_sct_testing_data = commands.getstatusoutput("echo $SCT_TESTING_DATA_DIR")
fname_anat = path_sct_testing_data + "/t2/t2.nii.gz"
fname_point = path_sct_testing_data + "/t2/t2_centerline_init.nii.gz"
slice_gap = 5
# check existence of input files
sct.check_file_exist(fname_anat)
sct.check_file_exist(fname_point)
# extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
path_point, file_point, ext_point = sct.extract_fname(fname_point)
# extract path of schedule file
# TODO: include schedule file in sct
# TODO: check existence of schedule file
file_schedule = path_sct + param.schedule_file
# Get input image orientation
input_image_orientation = get_orientation_3d(fname_anat, filename=True)
# Display arguments
print "\nCheck input arguments..."
print " Anatomical image: " + fname_anat
print " Orientation: " + input_image_orientation
print " Point in spinal cord: " + fname_point
print " Slice gap: " + str(slice_gap)
print " Gaussian kernel: " + str(gaussian_kernel)
print " Degree of polynomial: " + str(param.deg_poly)
# create temporary folder
print ("\nCreate temporary folder...")
path_tmp = "tmp." + strftime("%y%m%d%H%M%S")
sct.create_folder(path_tmp)
print "\nCopy input data..."
sct.run("cp " + fname_anat + " " + path_tmp + "/tmp.anat" + ext_anat)
sct.run("cp " + fname_point + " " + path_tmp + "/tmp.point" + ext_point)
# go to temporary folder
os.chdir(path_tmp)
# convert to nii
im_anat = convert("tmp.anat" + ext_anat, "tmp.anat.nii")
im_point = convert("tmp.point" + ext_point, "tmp.point.nii")
# Reorient input anatomical volume into RL PA IS orientation
print "\nReorient input volume to RL PA IS orientation..."
set_orientation(im_anat, "RPI")
im_anat.setFileName("tmp.anat_orient.nii")
# Reorient binary point into RL PA IS orientation
print "\nReorient binary point into RL PA IS orientation..."
# sct.run(sct.fsloutput + 'fslswapdim tmp.point RL PA IS tmp.point_orient')
set_orientation(im_point, "RPI")
im_point.setFileName("tmp.point_orient.nii")
# Get image dimensions
print "\nGet image dimensions..."
nx, ny, nz, nt, px, py, pz, pt = Image("tmp.anat_orient.nii").dim
print ".. matrix size: " + str(nx) + " x " + str(ny) + " x " + str(nz)
print ".. voxel size: " + str(px) + "mm x " + str(py) + "mm x " + str(pz) + "mm"
# Split input volume
print "\nSplit input volume..."
im_anat_split_list = split_data(im_anat, 2)
file_anat_split = []
for im in im_anat_split_list:
file_anat_split.append(im.absolutepath)
im.save()
im_point_split_list = split_data(im_point, 2)
file_point_split = []
for im in im_point_split_list:
file_point_split.append(im.absolutepath)
im.save()
# Extract coordinates of input point
data_point = Image("tmp.point_orient.nii").data
x_init, y_init, z_init = unravel_index(data_point.argmax(), data_point.shape)
sct.printv("Coordinates of input point: (" + str(x_init) + ", " + str(y_init) + ", " + str(z_init) + ")", verbose)
# Create 2D gaussian mask
sct.printv("\nCreate gaussian mask from point...", verbose)
xx, yy = mgrid[:nx, :ny]
mask2d = zeros((nx, ny))
radius = round(float(gaussian_kernel + 1) / 2) # add 1 because the radius includes the center.
sigma = float(radius)
mask2d = exp(-(((xx - x_init) ** 2) / (2 * (sigma ** 2)) + ((yy - y_init) ** 2) / (2 * (sigma ** 2))))
# Save mask to 2d file
file_mask_split = ["tmp.mask_orient_Z" + str(z).zfill(4) for z in range(0, nz, 1)]
nii_mask2d = Image("tmp.anat_orient_Z0000.nii")
nii_mask2d.data = mask2d
nii_mask2d.setFileName(file_mask_split[z_init] + ".nii")
nii_mask2d.save()
# initialize variables
file_mat = ["tmp.mat_Z" + str(z).zfill(4) for z in range(0, nz, 1)]
file_mat_inv = ["tmp.mat_inv_Z" + str(z).zfill(4) for z in range(0, nz, 1)]
file_mat_inv_cumul = ["tmp.mat_inv_cumul_Z" + str(z).zfill(4) for z in range(0, nz, 1)]
# create identity matrix for initial transformation matrix
fid = open(file_mat_inv_cumul[z_init], "w")
fid.write("%i %i %i %i\n" % (1, 0, 0, 0))
fid.write("%i %i %i %i\n" % (0, 1, 0, 0))
fid.write("%i %i %i %i\n" % (0, 0, 1, 0))
fid.write("%i %i %i %i\n" % (0, 0, 0, 1))
fid.close()
# initialize centerline: give value corresponding to initial point
x_centerline = [x_init]
y_centerline = [y_init]
z_centerline = [z_init]
warning_count = 0
# go up (1), then down (2) in reference to the binary point
for iUpDown in range(1, 3):
if iUpDown == 1:
# z increases
slice_gap_signed = slice_gap
elif iUpDown == 2:
# z decreases
slice_gap_signed = -slice_gap
# reverse centerline (because values will be appended at the end)
x_centerline.reverse()
y_centerline.reverse()
z_centerline.reverse()
# initialization before looping
z_dest = z_init # point given by user
z_src = z_dest + slice_gap_signed
# continue looping if 0 <= z < nz
while 0 <= z_src < nz:
# print current z:
print "z=" + str(z_src) + ":"
# estimate transformation
sct.run(
fsloutput
+ "flirt -in "
+ file_anat_split[z_src]
+ " -ref "
+ file_anat_split[z_dest]
+ " -schedule "
+ file_schedule
+ " -verbose 0 -omat "
+ file_mat[z_src]
+ " -cost normcorr -forcescaling -inweight "
+ file_mask_split[z_dest]
+ " -refweight "
+ file_mask_split[z_dest]
)
# display transfo
status, output = sct.run("cat " + file_mat[z_src])
print output
# check if transformation is bigger than 1.5x slice_gap
tx = float(output.split()[3])
ty = float(output.split()[7])
norm_txy = linalg.norm([tx, ty], ord=2)
if norm_txy > 1.5 * slice_gap:
print "WARNING: Transformation is too large --> using previous one."
warning_count = warning_count + 1
# if previous transformation exists, replace current one with previous one
if os.path.isfile(file_mat[z_dest]):
sct.run("cp " + file_mat[z_dest] + " " + file_mat[z_src])
# estimate inverse transformation matrix
sct.run("convert_xfm -omat " + file_mat_inv[z_src] + " -inverse " + file_mat[z_src])
# compute cumulative transformation
sct.run(
"convert_xfm -omat "
+ file_mat_inv_cumul[z_src]
+ " -concat "
+ file_mat_inv[z_src]
+ " "
+ file_mat_inv_cumul[z_dest]
)
# apply inverse cumulative transformation to initial gaussian mask (to put it in src space)
sct.run(
fsloutput
+ "flirt -in "
+ file_mask_split[z_init]
+ " -ref "
+ file_mask_split[z_init]
+ " -applyxfm -init "
+ file_mat_inv_cumul[z_src]
+ " -out "
+ file_mask_split[z_src]
)
# open inverse cumulative transformation file and generate centerline
fid = open(file_mat_inv_cumul[z_src])
mat = fid.read().split()
x_centerline.append(x_init + float(mat[3]))
y_centerline.append(y_init + float(mat[7]))
z_centerline.append(z_src)
# z_index = z_index+1
# define new z_dest (target slice) and new z_src (moving slice)
z_dest = z_dest + slice_gap_signed
z_src = z_src + slice_gap_signed
# Reconstruct centerline
# ====================================================================================================
# reverse back centerline (because it's been reversed once, so now all values are in the right order)
x_centerline.reverse()
y_centerline.reverse()
z_centerline.reverse()
# fit centerline in the Z-X plane using polynomial function
print "\nFit centerline in the Z-X plane using polynomial function..."
coeffsx = polyfit(z_centerline, x_centerline, deg=param.deg_poly)
polyx = poly1d(coeffsx)
x_centerline_fit = polyval(polyx, z_centerline)
# calculate RMSE
rmse = linalg.norm(x_centerline_fit - x_centerline) / sqrt(len(x_centerline))
# calculate max absolute error
max_abs = max(abs(x_centerline_fit - x_centerline))
print ".. RMSE (in mm): " + str(rmse * px)
print ".. Maximum absolute error (in mm): " + str(max_abs * px)
# fit centerline in the Z-Y plane using polynomial function
print "\nFit centerline in the Z-Y plane using polynomial function..."
coeffsy = polyfit(z_centerline, y_centerline, deg=param.deg_poly)
polyy = poly1d(coeffsy)
y_centerline_fit = polyval(polyy, z_centerline)
# calculate RMSE
rmse = linalg.norm(y_centerline_fit - y_centerline) / sqrt(len(y_centerline))
# calculate max absolute error
max_abs = max(abs(y_centerline_fit - y_centerline))
print ".. RMSE (in mm): " + str(rmse * py)
print ".. Maximum absolute error (in mm): " + str(max_abs * py)
# display
if param.debug == 1:
import matplotlib.pyplot as plt
plt.figure()
plt.plot(z_centerline, x_centerline, ".", z_centerline, x_centerline_fit, "r")
plt.legend(["Data", "Polynomial Fit"])
plt.title("Z-X plane polynomial interpolation")
plt.show()
plt.figure()
plt.plot(z_centerline, y_centerline, ".", z_centerline, y_centerline_fit, "r")
plt.legend(["Data", "Polynomial Fit"])
plt.title("Z-Y plane polynomial interpolation")
plt.show()
# generate full range z-values for centerline
z_centerline_full = [iz for iz in range(0, nz, 1)]
# calculate X and Y values for the full centerline
x_centerline_fit_full = polyval(polyx, z_centerline_full)
y_centerline_fit_full = polyval(polyy, z_centerline_full)
# Generate fitted transformation matrices and write centerline coordinates in text file
print "\nGenerate fitted transformation matrices and write centerline coordinates in text file..."
file_mat_inv_cumul_fit = ["tmp.mat_inv_cumul_fit_z" + str(z).zfill(4) for z in range(0, nz, 1)]
file_mat_cumul_fit = ["tmp.mat_cumul_fit_z" + str(z).zfill(4) for z in range(0, nz, 1)]
fid_centerline = open("tmp.centerline_coordinates.txt", "w")
for iz in range(0, nz, 1):
# compute inverse cumulative fitted transformation matrix
fid = open(file_mat_inv_cumul_fit[iz], "w")
fid.write("%i %i %i %f\n" % (1, 0, 0, x_centerline_fit_full[iz] - x_init))
fid.write("%i %i %i %f\n" % (0, 1, 0, y_centerline_fit_full[iz] - y_init))
fid.write("%i %i %i %i\n" % (0, 0, 1, 0))
fid.write("%i %i %i %i\n" % (0, 0, 0, 1))
fid.close()
# compute forward cumulative fitted transformation matrix
sct.run("convert_xfm -omat " + file_mat_cumul_fit[iz] + " -inverse " + file_mat_inv_cumul_fit[iz])
# write centerline coordinates in x, y, z format
fid_centerline.write(
"%f %f %f\n" % (x_centerline_fit_full[iz], y_centerline_fit_full[iz], z_centerline_full[iz])
)
fid_centerline.close()
# Prepare output data
# ====================================================================================================
# write centerline as text file
for iz in range(0, nz, 1):
# compute inverse cumulative fitted transformation matrix
fid = open(file_mat_inv_cumul_fit[iz], "w")
fid.write("%i %i %i %f\n" % (1, 0, 0, x_centerline_fit_full[iz] - x_init))
fid.write("%i %i %i %f\n" % (0, 1, 0, y_centerline_fit_full[iz] - y_init))
fid.write("%i %i %i %i\n" % (0, 0, 1, 0))
fid.write("%i %i %i %i\n" % (0, 0, 0, 1))
fid.close()
# write polynomial coefficients
savetxt("tmp.centerline_polycoeffs_x.txt", coeffsx)
savetxt("tmp.centerline_polycoeffs_y.txt", coeffsy)
# apply transformations to data
print "\nApply fitted transformation matrices..."
file_anat_split_fit = ["tmp.anat_orient_fit_z" + str(z).zfill(4) for z in range(0, nz, 1)]
file_mask_split_fit = ["tmp.mask_orient_fit_z" + str(z).zfill(4) for z in range(0, nz, 1)]
file_point_split_fit = ["tmp.point_orient_fit_z" + str(z).zfill(4) for z in range(0, nz, 1)]
for iz in range(0, nz, 1):
# forward cumulative transformation to data
sct.run(
fsloutput
+ "flirt -in "
+ file_anat_split[iz]
+ " -ref "
+ file_anat_split[iz]
+ " -applyxfm -init "
+ file_mat_cumul_fit[iz]
+ " -out "
+ file_anat_split_fit[iz]
)
# inverse cumulative transformation to mask
sct.run(
fsloutput
+ "flirt -in "
+ file_mask_split[z_init]
+ " -ref "
+ file_mask_split[z_init]
+ " -applyxfm -init "
+ file_mat_inv_cumul_fit[iz]
+ " -out "
+ file_mask_split_fit[iz]
)
# inverse cumulative transformation to point
sct.run(
fsloutput
+ "flirt -in "
+ file_point_split[z_init]
+ " -ref "
+ file_point_split[z_init]
+ " -applyxfm -init "
+ file_mat_inv_cumul_fit[iz]
+ " -out "
+ file_point_split_fit[iz]
+ " -interp nearestneighbour"
)
# Merge into 4D volume
print "\nMerge into 4D volume..."
# im_anat_list = [Image(fname) for fname in glob.glob('tmp.anat_orient_fit_z*.nii')]
fname_anat_list = glob.glob("tmp.anat_orient_fit_z*.nii")
im_anat_concat = concat_data(fname_anat_list, 2)
im_anat_concat.setFileName("tmp.anat_orient_fit.nii")
im_anat_concat.save()
# im_mask_list = [Image(fname) for fname in glob.glob('tmp.mask_orient_fit_z*.nii')]
fname_mask_list = glob.glob("tmp.mask_orient_fit_z*.nii")
im_mask_concat = concat_data(fname_mask_list, 2)
im_mask_concat.setFileName("tmp.mask_orient_fit.nii")
im_mask_concat.save()
# im_point_list = [Image(fname) for fname in glob.glob('tmp.point_orient_fit_z*.nii')]
fname_point_list = glob.glob("tmp.point_orient_fit_z*.nii")
im_point_concat = concat_data(fname_point_list, 2)
im_point_concat.setFileName("tmp.point_orient_fit.nii")
im_point_concat.save()
# Copy header geometry from input data
print "\nCopy header geometry from input data..."
im_anat = Image("tmp.anat_orient.nii")
im_anat_orient_fit = Image("tmp.anat_orient_fit.nii")
im_mask_orient_fit = Image("tmp.mask_orient_fit.nii")
im_point_orient_fit = Image("tmp.point_orient_fit.nii")
im_anat_orient_fit = copy_header(im_anat, im_anat_orient_fit)
im_mask_orient_fit = copy_header(im_anat, im_mask_orient_fit)
im_point_orient_fit = copy_header(im_anat, im_point_orient_fit)
for im in [im_anat_orient_fit, im_mask_orient_fit, im_point_orient_fit]:
im.save()
# Reorient outputs into the initial orientation of the input image
print "\nReorient the centerline into the initial orientation of the input image..."
set_orientation("tmp.point_orient_fit.nii", input_image_orientation, "tmp.point_orient_fit.nii")
set_orientation("tmp.mask_orient_fit.nii", input_image_orientation, "tmp.mask_orient_fit.nii")
# Generate output file (in current folder)
print "\nGenerate output file (in current folder)..."
os.chdir("..") # come back to parent folder
fname_output_centerline = sct.generate_output_file(
path_tmp + "/tmp.point_orient_fit.nii", file_anat + "_centerline" + ext_anat
)
# Delete temporary files
if remove_tmp_files == 1:
print "\nRemove temporary files..."
sct.run("rm -rf " + path_tmp, error_exit="warning")
# print number of warnings
print "\nNumber of warnings: " + str(
warning_count
) + " (if >10, you should probably reduce the gap and/or increase the kernel size"
# display elapsed time
elapsed_time = time() - start_time
print "\nFinished! \n\tGenerated file: " + fname_output_centerline + "\n\tElapsed time: " + str(
int(round(elapsed_time))
) + "s\n"
def compute_dti(fname_in, fname_bvals, fname_bvecs, prefix, method, file_mask):
"""
Compute DTI.
:param fname_in: input 4d file.
:param bvals: bvals txt file
:param bvecs: bvecs txt file
:param prefix: output prefix. Example: "dti_"
:param method: algo for computing dti
:return: True/False
"""
# Open file.
from msct_image import Image
nii = Image(fname_in)
data = nii.data
sct.printv('data.shape (%d, %d, %d, %d)' % data.shape)
# open bvecs/bvals
from dipy.io import read_bvals_bvecs
bvals, bvecs = read_bvals_bvecs(fname_bvals, fname_bvecs)
from dipy.core.gradients import gradient_table
gtab = gradient_table(bvals, bvecs)
# mask and crop the data. This is a quick way to avoid calculating Tensors on the background of the image.
if not file_mask == '':
sct.printv('Open mask file...', param.verbose)
# open mask file
nii_mask = Image(file_mask)
mask = nii_mask.data
# fit tensor model
sct.printv('Computing tensor using "' + method + '" method...',
param.verbose)
import dipy.reconst.dti as dti
if method == 'standard':
tenmodel = dti.TensorModel(gtab)
if file_mask == '':
tenfit = tenmodel.fit(data)
else:
tenfit = tenmodel.fit(data, mask)
elif method == 'restore':
import dipy.denoise.noise_estimate as ne
sigma = ne.estimate_sigma(data)
dti_restore = dti.TensorModel(gtab, fit_method='RESTORE', sigma=sigma)
if file_mask == '':
tenfit = dti_restore.fit(data)
else:
tenfit = dti_restore.fit(data, mask)
# Compute metrics
sct.printv('Computing metrics...', param.verbose)
# FA
from dipy.reconst.dti import fractional_anisotropy
nii.data = fractional_anisotropy(tenfit.evals)
nii.setFileName(prefix + 'FA.nii.gz')
nii.save('float32')
# MD
from dipy.reconst.dti import mean_diffusivity
nii.data = mean_diffusivity(tenfit.evals)
nii.setFileName(prefix + 'MD.nii.gz')
nii.save('float32')
# RD
from dipy.reconst.dti import radial_diffusivity
nii.data = radial_diffusivity(tenfit.evals)
nii.setFileName(prefix + 'RD.nii.gz')
nii.save('float32')
# AD
from dipy.reconst.dti import axial_diffusivity
nii.data = axial_diffusivity(tenfit.evals)
nii.setFileName(prefix + 'AD.nii.gz')
nii.save('float32')
return True
def main():
# Initialization
fname_data = ''
suffix_out = '_crop'
remove_temp_files = param.remove_temp_files
verbose = param.verbose
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
remove_temp_files = param.remove_temp_files
# Parameters for debug mode
if param.debug:
print '\n*** WARNING: DEBUG MODE ON ***\n'
fname_data = path_sct + '/testing/data/errsm_23/t2/t2.nii.gz'
remove_temp_files = 0
else:
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'hi:r:v:')
except getopt.GetoptError:
usage()
if not opts:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-i'):
fname_data = arg
elif opt in ('-r'):
remove_temp_files = int(arg)
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname_data == '':
usage()
# Check file existence
sct.printv('\nCheck file existence...', verbose)
sct.check_file_exist(fname_data, verbose)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_data).dim
sct.printv('.. ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), verbose)
# check if 4D data
if not nt == 1:
sct.printv(
'\nERROR in ' + os.path.basename(__file__) +
': Data should be 3D.\n', 1, 'error')
sys.exit(2)
# print arguments
print '\nCheck parameters:'
print ' data ................... ' + fname_data
print
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
path_out, file_out, ext_out = '', file_data + suffix_out, ext_data
# create temporary folder
path_tmp = 'tmp.' + time.strftime("%y%m%d%H%M%S") + '/'
sct.run('mkdir ' + path_tmp)
# copy files into tmp folder
sct.run('isct_c3d ' + fname_data + ' -o ' + path_tmp + 'data.nii')
# go to tmp folder
os.chdir(path_tmp)
# change orientation
sct.printv('\nChange orientation to RPI...', verbose)
set_orientation('data.nii', 'RPI', 'data_rpi.nii')
# get image of medial slab
sct.printv('\nGet image of medial slab...', verbose)
image_array = nibabel.load('data_rpi.nii').get_data()
nx, ny, nz = image_array.shape
scipy.misc.imsave('image.jpg', image_array[math.floor(nx / 2), :, :])
# Display the image
sct.printv('\nDisplay image and get cropping region...', verbose)
fig = plt.figure()
# fig = plt.gcf()
# ax = plt.gca()
ax = fig.add_subplot(111)
img = mpimg.imread("image.jpg")
implot = ax.imshow(img.T)
implot.set_cmap('gray')
plt.gca().invert_yaxis()
# mouse callback
ax.set_title(
'Left click on the top and bottom of your cropping field.\n Right click to remove last point.\n Close window when your done.'
)
line, = ax.plot([], [], 'ro') # empty line
cropping_coordinates = LineBuilder(line)
plt.show()
# disconnect callback
# fig.canvas.mpl_disconnect(line)
# check if user clicked two times
if len(cropping_coordinates.xs) != 2:
sct.printv('\nERROR: You have to select two points. Exit program.\n',
1, 'error')
sys.exit(2)
# convert coordinates to integer
zcrop = [int(i) for i in cropping_coordinates.ys]
# sort coordinates
zcrop.sort()
# crop image
sct.printv('\nCrop image...', verbose)
nii = Image('data_rpi.nii')
data_crop = nii.data[:, :, zcrop[0]:zcrop[1]]
nii.data = data_crop
nii.setFileName('data_rpi_crop.nii')
nii.save()
# come back to parent folder
os.chdir('..')
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(path_tmp + 'data_rpi_crop.nii',
path_out + file_out + ext_out)
# Remove temporary files
if remove_temp_files == 1:
print('\nRemove temporary files...')
sct.run('rm -rf ' + path_tmp)
# to view results
print '\nDone! To view results, type:'
print 'fslview ' + path_out + file_out + ext_out + ' &'
print
def compute_csa(fname_segmentation, verbose, remove_temp_files, step, smoothing_param, figure_fit, file_csa_volume, slices, vert_levels, fname_vertebral_labeling='', algo_fitting = 'hanning', type_window = 'hanning', window_length = 80):
# Extract path, file and extension
fname_segmentation = os.path.abspath(fname_segmentation)
path_data, file_data, ext_data = sct.extract_fname(fname_segmentation)
# create temporary folder
sct.printv('\nCreate temporary folder...', verbose)
path_tmp = sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S") + '_'+str(randint(1, 1000000)), 1)
sct.run('mkdir '+path_tmp, verbose)
# Copying input data to tmp folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
sct.run('sct_convert -i '+fname_segmentation+' -o '+path_tmp+'segmentation.nii.gz', verbose)
# go to tmp folder
os.chdir(path_tmp)
# Change orientation of the input segmentation into RPI
sct.printv('\nChange orientation to RPI...', verbose)
sct.run('sct_image -i segmentation.nii.gz -setorient RPI -o segmentation_RPI.nii.gz', verbose)
# Open segmentation volume
sct.printv('\nOpen segmentation volume...', verbose)
im_seg = Image('segmentation_RPI.nii.gz')
data_seg = im_seg.data
# hdr_seg = im_seg.hdr
# Get size of data
sct.printv('\nGet data dimensions...', verbose)
nx, ny, nz, nt, px, py, pz, pt = im_seg.dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), verbose)
# # Extract min and max index in Z direction
X, Y, Z = (data_seg > 0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
# extract centerline and smooth it
x_centerline_fit, y_centerline_fit, z_centerline, x_centerline_deriv, y_centerline_deriv, z_centerline_deriv = smooth_centerline('segmentation_RPI.nii.gz', algo_fitting=algo_fitting, type_window=type_window, window_length=window_length, verbose=verbose)
z_centerline_scaled = [x*pz for x in z_centerline]
# Compute CSA
sct.printv('\nCompute CSA...', verbose)
# Empty arrays in which CSA for each z slice will be stored
csa = np.zeros(max_z_index-min_z_index+1)
for iz in xrange(min_z_index, max_z_index+1):
# compute the vector normal to the plane
normal = normalize(np.array([x_centerline_deriv[iz-min_z_index], y_centerline_deriv[iz-min_z_index], z_centerline_deriv[iz-min_z_index]]))
# compute the angle between the normal vector of the plane and the vector z
angle = np.arccos(np.dot(normal, [0, 0, 1]))
# compute the number of voxels, assuming the segmentation is coded for partial volume effect between 0 and 1.
number_voxels = np.sum(data_seg[:, :, iz])
# compute CSA, by scaling with voxel size (in mm) and adjusting for oblique plane
csa[iz-min_z_index] = number_voxels * px * py * np.cos(angle)
sct.printv('\nSmooth CSA across slices...', verbose)
if smoothing_param:
from msct_smooth import smoothing_window
sct.printv('.. Hanning window: '+str(smoothing_param)+' mm', verbose)
csa_smooth = smoothing_window(csa, window_len=smoothing_param/pz, window='hanning', verbose=0)
# display figure
if verbose == 2:
import matplotlib.pyplot as plt
plt.figure()
pltx, = plt.plot(z_centerline_scaled, csa, 'bo')
pltx_fit, = plt.plot(z_centerline_scaled, csa_smooth, 'r', linewidth=2)
plt.title("Cross-sectional area (CSA)")
plt.xlabel('z (mm)')
plt.ylabel('CSA (mm^2)')
plt.legend([pltx, pltx_fit], ['Raw', 'Smoothed'])
plt.show()
# update variable
csa = csa_smooth
else:
sct.printv('.. No smoothing!', verbose)
# Create output text file
sct.printv('\nWrite text file...', verbose)
file_results = open('csa.txt', 'w')
for i in range(min_z_index, max_z_index+1):
file_results.write(str(int(i)) + ',' + str(csa[i-min_z_index])+'\n')
# Display results
sct.printv('z='+str(i-min_z_index)+': '+str(csa[i-min_z_index])+' mm^2', verbose, 'bold')
file_results.close()
# output volume of csa values
sct.printv('\nCreate volume of CSA values...', verbose)
data_csa = data_seg.astype(np.float32, copy=False)
# loop across slices
for iz in range(min_z_index, max_z_index+1):
# retrieve seg pixels
x_seg, y_seg = (data_csa[:, :, iz] > 0).nonzero()
seg = [[x_seg[i],y_seg[i]] for i in range(0, len(x_seg))]
# loop across pixels in segmentation
for i in seg:
# replace value with csa value
data_csa[i[0], i[1], iz] = csa[iz-min_z_index]
# replace data
im_seg.data = data_csa
# set original orientation
# TODO: FIND ANOTHER WAY!!
# im_seg.change_orientation(orientation) --> DOES NOT WORK!
# set file name -- use .gz because faster to write
im_seg.setFileName('csa_volume_RPI.nii.gz')
im_seg.changeType('float32')
# save volume
im_seg.save()
# get orientation of the input data
im_seg_original = Image('segmentation.nii.gz')
orientation = im_seg_original.orientation
sct.run('sct_image -i csa_volume_RPI.nii.gz -setorient '+orientation+' -o '+file_csa_volume)
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', verbose)
copyfile(path_tmp+'csa.txt', path_data+param.fname_csa)
# sct.generate_output_file(path_tmp+'csa.txt', path_data+param.fname_csa) # extension already included in param.fname_csa
sct.generate_output_file(path_tmp+file_csa_volume, path_data+file_csa_volume) # extension already included in name_output
# average csa across vertebral levels or slices if asked (flag -z or -l)
if slices or vert_levels:
from sct_extract_metric import save_metrics
warning = ''
if vert_levels and not fname_vertebral_labeling:
sct.printv('\nERROR: Vertebral labeling file is missing. See usage.\n', 1, 'error')
elif vert_levels and fname_vertebral_labeling:
# from sct_extract_metric import get_slices_matching_with_vertebral_levels
sct.printv('\tSelected vertebral levels... '+vert_levels)
# convert the vertebral labeling file to RPI orientation
im_vertebral_labeling = set_orientation(Image(fname_vertebral_labeling), 'RPI', fname_out=path_tmp+'vertebral_labeling_RPI.nii')
# get the slices corresponding to the vertebral levels
# slices, vert_levels_list, warning = get_slices_matching_with_vertebral_levels(data_seg, vert_levels, im_vertebral_labeling.data, 1)
slices, vert_levels_list, warning = get_slices_matching_with_vertebral_levels_based_centerline(vert_levels, im_vertebral_labeling.data, x_centerline_fit, y_centerline_fit, z_centerline)
elif not vert_levels:
vert_levels_list = []
sct.printv('Average CSA across slices...', type='info')
# parse the selected slices
slices_lim = slices.strip().split(':')
slices_list = range(int(slices_lim[0]), int(slices_lim[1])+1)
CSA_for_selected_slices = []
# Read the file csa.txt and get the CSA for the selected slices
with open(path_data+param.fname_csa) as openfile:
for line in openfile:
line_split = line.strip().split(',')
if int(line_split[0]) in slices_list:
CSA_for_selected_slices.append(float(line_split[1]))
# average the CSA
mean_CSA = np.mean(np.asarray(CSA_for_selected_slices))
std_CSA = np.std(np.asarray(CSA_for_selected_slices))
sct.printv('Mean CSA: '+str(mean_CSA)+' +/- '+str(std_CSA)+' mm^2', type='info')
# write result into output file
save_metrics([0], [file_data], slices, [mean_CSA], [std_CSA], path_data + 'csa_mean.txt', path_data+file_csa_volume, 'nb_voxels x px x py x cos(theta) slice-by-slice (in mm^3)', '', actual_vert=vert_levels_list, warning_vert_levels=warning)
# compute volume between the selected slices
sct.printv('Compute the volume in between the selected slices...', type='info')
nb_vox = np.sum(data_seg[:, :, slices_list])
volume = nb_vox*px*py*pz
sct.printv('Volume in between the selected slices: '+str(volume)+' mm^3', type='info')
# write result into output file
save_metrics([0], [file_data], slices, [volume], [np.nan], path_data + 'volume.txt', path_data+file_data, 'nb_voxels x px x py x pz (in mm^3)', '', actual_vert=vert_levels_list, warning_vert_levels=warning)
# Remove temporary files
if remove_temp_files:
sct.printv('\nRemove temporary files...')
sct.run('rm -rf '+path_tmp, error_exit='warning')
def extract_centerline(fname_segmentation, remove_temp_files, verbose = 0, algo_fitting = 'hanning', type_window = 'hanning', window_length = 80):
# Extract path, file and extension
fname_segmentation = os.path.abspath(fname_segmentation)
path_data, file_data, ext_data = sct.extract_fname(fname_segmentation)
# create temporary folder
sct.printv('\nCreate temporary folder...', verbose)
path_tmp = sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S") + '_'+str(randint(1, 1000000)), 1)
sct.run('mkdir '+path_tmp, verbose)
# Copying input data to tmp folder
sct.printv('\nCopying data to tmp folder...', verbose)
sct.run('sct_convert -i '+fname_segmentation+' -o '+path_tmp+'segmentation.nii.gz', verbose)
# go to tmp folder
os.chdir(path_tmp)
# Change orientation of the input centerline into RPI
sct.printv('\nOrient centerline to RPI orientation...', verbose)
# fname_segmentation_orient = 'segmentation_RPI.nii.gz'
# BELOW DOES NOT WORK (JULIEN, 2015-10-17)
# im_seg = Image(file_data+ext_data)
# set_orientation(im_seg, 'RPI')
# im_seg.setFileName(fname_segmentation_orient)
# im_seg.save()
sct.run('sct_image -i segmentation.nii.gz -setorient RPI -o segmentation_RPI.nii.gz', verbose)
# Open segmentation volume
sct.printv('\nOpen segmentation volume...', verbose)
im_seg = Image('segmentation_RPI.nii.gz')
data = im_seg.data
# Get size of data
sct.printv('\nGet data dimensions...', verbose)
nx, ny, nz, nt, px, py, pz, pt = im_seg.dim
sct.printv('.. matrix size: '+str(nx)+' x '+str(ny)+' x '+str(nz), verbose)
sct.printv('.. voxel size: '+str(px)+'mm x '+str(py)+'mm x '+str(pz)+'mm', verbose)
# # Get dimension
# sct.printv('\nGet dimensions...', verbose)
# nx, ny, nz, nt, px, py, pz, pt = im_seg.dim
#
# # Extract orientation of the input segmentation
# orientation = get_orientation(im_seg)
# sct.printv('\nOrientation of segmentation image: ' + orientation, verbose)
#
# sct.printv('\nOpen segmentation volume...', verbose)
# data = im_seg.data
# hdr = im_seg.hdr
# Extract min and max index in Z direction
X, Y, Z = (data>0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
x_centerline = [0 for i in range(0,max_z_index-min_z_index+1)]
y_centerline = [0 for i in range(0,max_z_index-min_z_index+1)]
z_centerline = [iz for iz in range(min_z_index, max_z_index+1)]
# Extract segmentation points and average per slice
for iz in range(min_z_index, max_z_index+1):
x_seg, y_seg = (data[:,:,iz]>0).nonzero()
x_centerline[iz-min_z_index] = np.mean(x_seg)
y_centerline[iz-min_z_index] = np.mean(y_seg)
for k in range(len(X)):
data[X[k], Y[k], Z[k]] = 0
# extract centerline and smooth it
x_centerline_fit, y_centerline_fit, z_centerline_fit, x_centerline_deriv, y_centerline_deriv, z_centerline_deriv = smooth_centerline('segmentation_RPI.nii.gz', type_window = type_window, window_length = window_length, algo_fitting = algo_fitting, verbose = verbose)
if verbose == 2:
import matplotlib.pyplot as plt
#Creation of a vector x that takes into account the distance between the labels
nz_nonz = len(z_centerline)
x_display = [0 for i in range(x_centerline_fit.shape[0])]
y_display = [0 for i in range(y_centerline_fit.shape[0])]
for i in range(0, nz_nonz, 1):
x_display[int(z_centerline[i]-z_centerline[0])] = x_centerline[i]
y_display[int(z_centerline[i]-z_centerline[0])] = y_centerline[i]
plt.figure(1)
plt.subplot(2,1,1)
plt.plot(z_centerline_fit, x_display, 'ro')
plt.plot(z_centerline_fit, x_centerline_fit)
plt.xlabel("Z")
plt.ylabel("X")
plt.title("x and x_fit coordinates")
plt.subplot(2,1,2)
plt.plot(z_centerline_fit, y_display, 'ro')
plt.plot(z_centerline_fit, y_centerline_fit)
plt.xlabel("Z")
plt.ylabel("Y")
plt.title("y and y_fit coordinates")
plt.show()
# Create an image with the centerline
for iz in range(min_z_index, max_z_index+1):
data[round(x_centerline_fit[iz-min_z_index]), round(y_centerline_fit[iz-min_z_index]), iz] = 1 # if index is out of bounds here for hanning: either the segmentation has holes or labels have been added to the file
# Write the centerline image in RPI orientation
# hdr.set_data_dtype('uint8') # set imagetype to uint8
sct.printv('\nWrite NIFTI volumes...', verbose)
im_seg.data = data
im_seg.setFileName('centerline_RPI.nii.gz')
im_seg.changeType('uint8')
im_seg.save()
sct.printv('\nSet to original orientation...', verbose)
# get orientation of the input data
im_seg_original = Image('segmentation.nii.gz')
orientation = im_seg_original.orientation
sct.run('sct_image -i centerline_RPI.nii.gz -setorient '+orientation+' -o centerline.nii.gz')
# create a txt file with the centerline
name_output_txt = 'centerline.txt'
sct.printv('\nWrite text file...', verbose)
file_results = open(name_output_txt, 'w')
for i in range(min_z_index, max_z_index+1):
file_results.write(str(int(i)) + ' ' + str(x_centerline_fit[i-min_z_index]) + ' ' + str(y_centerline_fit[i-min_z_index]) + '\n')
file_results.close()
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(path_tmp+'centerline.nii.gz', file_data+'_centerline.nii.gz')
sct.generate_output_file(path_tmp+'centerline.txt', file_data+'_centerline.txt')
# Remove temporary files
if remove_temp_files:
sct.printv('\nRemove temporary files...', verbose)
sct.run('rm -rf '+path_tmp, verbose)
return file_data+'_centerline.nii.gz'
zmin_cord = np.min(np.nonzero(zsum))
zmax_cord = np.max(np.nonzero(zsum))
# duplicate WM and GM atlas towards the top and bottom slices to match the cord template
# bottom slices
for iz in range(zmin_cord, zmin_wm):
data_wm[:, :, iz] = data_wm[:, :, zmin_wm]
data_gm[:, :, iz] = data_gm[:, :, zmin_wm]
# top slices
for iz in range(zmax_wm, zmax_cord):
data_wm[:, :, iz] = data_wm[:, :, zmax_wm]
data_gm[:, :, iz] = data_gm[:, :, zmax_wm]
# save modified atlases
im_wm.setFileName('wm_ext.nii.gz')
im_wm.data = data_wm
im_wm.save()
im_gm.setFileName('gm_ext.nii.gz')
im_gm.data = data_gm
im_gm.save()
# sum modified wm/gm
data_wmgm = data_wm + data_gm
# save wm/gm
im_wm.setFileName('wmgm_ext.nii.gz')
im_wm.data = data_wmgm
im_wm.save()
# register wmgm --> cord
sct.run('cp '+fname_cord+' cord.nii.gz')
def main(args=None):
dim_list = ['x', 'y', 'z', 't']
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(args)
fname_in = arguments["-i"]
fname_out = arguments["-o"]
verbose = int(arguments['-v'])
if '-type' in arguments:
output_type = arguments['-type']
else:
output_type = ''
# Open file(s)
im = Image(fname_in)
data = im.data # 3d or 4d numpy array
dim = im.dim
# run command
if '-otsu' in arguments:
param = arguments['-otsu']
data_out = otsu(data, param)
elif '-otsu_adap' in arguments:
param = arguments['-otsu_adap']
data_out = otsu_adap(data, param[0], param[1])
elif '-otsu_median' in arguments:
param = arguments['-otsu_median']
data_out = otsu_median(data, param[0], param[1])
elif '-thr' in arguments:
param = arguments['-thr']
data_out = threshold(data, param)
elif '-percent' in arguments:
param = arguments['-percent']
data_out = perc(data, param)
elif '-bin' in arguments:
bin_thr = arguments['-bin']
data_out = binarise(data, bin_thr=bin_thr)
elif '-add' in arguments:
from numpy import sum
data2 = get_data_or_scalar(arguments["-add"], data)
data_concat = concatenate_along_4th_dimension(data, data2)
data_out = sum(data_concat, axis=3)
elif '-sub' in arguments:
data2 = get_data_or_scalar(arguments['-sub'], data)
data_out = data - data2
elif "-laplacian" in arguments:
sigmas = arguments["-laplacian"]
if len(sigmas) == 1:
sigmas = [sigmas for i in range(len(data.shape))]
elif len(sigmas) != len(data.shape):
printv(
parser.usage.generate(
error=
'ERROR: -laplacian need the same number of inputs as the number of image dimension OR only one input'
))
# adjust sigma based on voxel size
sigmas = [sigmas[i] / dim[i + 4] for i in range(3)]
# smooth data
data_out = laplacian(data, sigmas)
elif '-mul' in arguments:
from numpy import prod
data2 = get_data_or_scalar(arguments["-mul"], data)
data_concat = concatenate_along_4th_dimension(data, data2)
data_out = prod(data_concat, axis=3)
elif '-div' in arguments:
from numpy import divide
data2 = get_data_or_scalar(arguments["-div"], data)
data_out = divide(data, data2)
elif '-mean' in arguments:
from numpy import mean
dim = dim_list.index(arguments['-mean'])
if dim + 1 > len(
np.shape(data)): # in case input volume is 3d and dim=t
data = data[..., np.newaxis]
data_out = mean(data, dim)
elif '-rms' in arguments:
from numpy import mean, sqrt, square
dim = dim_list.index(arguments['-rms'])
if dim + 1 > len(
np.shape(data)): # in case input volume is 3d and dim=t
data = data[..., np.newaxis]
data_out = sqrt(mean(square(data.astype(float)), dim))
elif '-std' in arguments:
from numpy import std
dim = dim_list.index(arguments['-std'])
if dim + 1 > len(
np.shape(data)): # in case input volume is 3d and dim=t
data = data[..., np.newaxis]
data_out = std(data, dim, ddof=1)
elif "-smooth" in arguments:
sigmas = arguments["-smooth"]
if len(sigmas) == 1:
sigmas = [sigmas[0] for i in range(len(data.shape))]
elif len(sigmas) != len(data.shape):
printv(
parser.usage.generate(
error=
'ERROR: -smooth need the same number of inputs as the number of image dimension OR only one input'
))
# adjust sigma based on voxel size
sigmas = [sigmas[i] / dim[i + 4] for i in range(3)]
# smooth data
data_out = smooth(data, sigmas)
elif '-dilate' in arguments:
data_out = dilate(data, arguments['-dilate'])
elif '-erode' in arguments:
data_out = erode(data, arguments['-erode'])
elif '-denoise' in arguments:
# parse denoising arguments
p, b = 1, 5 # default arguments
list_denoise = arguments['-denoise']
for i in list_denoise:
if 'p' in i:
p = int(i.split('=')[1])
if 'b' in i:
b = int(i.split('=')[1])
data_out = denoise_nlmeans(data, patch_radius=p, block_radius=b)
elif '-symmetrize' in arguments:
data_out = (data +
data[range(data.shape[0] - 1, -1, -1), :, :]) / float(2)
elif '-mi' in arguments:
# input 1 = from flag -i --> im
# input 2 = from flag -mi
im_2 = Image(arguments['-mi'])
compute_similarity(im.data,
im_2.data,
fname_out,
metric='mi',
verbose=verbose)
data_out = None
elif '-minorm' in arguments:
im_2 = Image(arguments['-minorm'])
compute_similarity(im.data,
im_2.data,
fname_out,
metric='minorm',
verbose=verbose)
data_out = None
elif '-corr' in arguments:
# input 1 = from flag -i --> im
# input 2 = from flag -mi
im_2 = Image(arguments['-corr'])
compute_similarity(im.data,
im_2.data,
fname_out,
metric='corr',
verbose=verbose)
data_out = None
# if no flag is set
else:
data_out = None
printv(
parser.usage.generate(
error=
'ERROR: you need to specify an operation to do on the input image'
))
if data_out is not None:
# Write output
nii_out = Image(fname_in) # use header of input file
nii_out.data = data_out
nii_out.setFileName(fname_out)
nii_out.save(type=output_type)
# TODO: case of multiple outputs
# assert len(data_out) == n_out
# if n_in == n_out:
# for im_in, d_out, fn_out in zip(nii, data_out, fname_out):
# im_in.data = d_out
# im_in.setFileName(fn_out)
# if "-w" in arguments:
# im_in.hdr.set_intent('vector', (), '')
# im_in.save()
# elif n_out == 1:
# nii[0].data = data_out[0]
# nii[0].setFileName(fname_out[0])
# if "-w" in arguments:
# nii[0].hdr.set_intent('vector', (), '')
# nii[0].save()
# elif n_out > n_in:
# for dat_out, name_out in zip(data_out, fname_out):
# im_out = nii[0].copy()
# im_out.data = dat_out
# im_out.setFileName(name_out)
# if "-w" in arguments:
# im_out.hdr.set_intent('vector', (), '')
# im_out.save()
# else:
# printv(parser.usage.generate(error='ERROR: not the correct numbers of inputs and outputs'))
# display message
if data_out is not None:
sct.display_viewer_syntax([fname_out])
else:
printv('\nDone! File created: ' + fname_out, verbose, 'info')
def register(src, dest, paramreg, param, i_step_str):
# initiate default parameters of antsRegistration transformation
ants_registration_params = {
'rigid': '',
'affine': '',
'compositeaffine': '',
'similarity': '',
'translation': '',
'bspline': ',10',
'gaussiandisplacementfield': ',3,0',
'bsplinedisplacementfield': ',5,10',
'syn': ',3,0',
'bsplinesyn': ',1,3'
}
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI'
# set metricSize
if paramreg.steps[i_step_str].metric == 'MI':
metricSize = '32' # corresponds to number of bins
else:
metricSize = '4' # corresponds to radius (for CC, MeanSquares...)
# set masking
if param.fname_mask:
fname_mask = 'mask.nii.gz'
masking = '-x mask.nii.gz'
else:
fname_mask = ''
masking = ''
if paramreg.steps[i_step_str].algo == 'slicereg':
from msct_image import find_zmin_zmax
# threshold images (otherwise, automatic crop does not work -- see issue #293)
src_th = sct.add_suffix(src, '_th')
from msct_image import Image
nii = Image(src)
data = nii.data
data[data < 0.1] = 0
nii.data = data
nii.setFileName(src_th)
nii.save()
# sct.run(fsloutput+'fslmaths '+src+' -thr 0.1 '+src_th, param.verbose)
dest_th = sct.add_suffix(dest, '_th')
nii = Image(dest)
data = nii.data
data[data < 0.1] = 0
nii.data = data
nii.setFileName(dest_th)
nii.save()
# sct.run(fsloutput+'fslmaths '+dest+' -thr 0.1 '+dest_th, param.verbose)
# find zmin and zmax
zmin_src, zmax_src = find_zmin_zmax(src_th)
zmin_dest, zmax_dest = find_zmin_zmax(dest_th)
zmin_total = max([zmin_src, zmin_dest])
zmax_total = min([zmax_src, zmax_dest])
# crop data
src_crop = sct.add_suffix(src, '_crop')
sct.run(
'sct_crop_image -i ' + src + ' -o ' + src_crop +
' -dim 2 -start ' + str(zmin_total) + ' -end ' + str(zmax_total),
param.verbose)
dest_crop = sct.add_suffix(dest, '_crop')
sct.run(
'sct_crop_image -i ' + dest + ' -o ' + dest_crop +
' -dim 2 -start ' + str(zmin_total) + ' -end ' + str(zmax_total),
param.verbose)
# update variables
src = src_crop
dest = dest_crop
# estimate transfo
cmd = (
'isct_antsSliceRegularizedRegistration '
'-t Translation[0.5] '
'-m ' + paramreg.steps[i_step_str].metric + '[' + dest + ',' +
src + ',1,' + metricSize + ',Regular,0.2] '
'-p ' + paramreg.steps[i_step_str].poly + ' '
'-i ' + paramreg.steps[i_step_str].iter + ' '
'-f 1 '
'-s ' + paramreg.steps[i_step_str].smooth + ' '
'-v 1 ' # verbose (verbose=2 does not exist, so we force it to 1)
'-o [step' + i_step_str + ',' + src + '_regStep' + i_step_str +
'.nii] ' # here the warp name is stage10 because antsSliceReg add "Warp"
+ masking)
warp_forward_out = 'step' + i_step_str + 'Warp.nii.gz'
warp_inverse_out = 'step' + i_step_str + 'InverseWarp.nii.gz'
elif paramreg.steps[i_step_str].algo == 'slicereg2d_pointwise':
from msct_register import register_slicereg2d_pointwise
warp_forward_out = 'step' + i_step_str + 'Warp.nii.gz'
warp_inverse_out = 'step' + i_step_str + 'InverseWarp.nii.gz'
register_slicereg2d_pointwise(
src,
dest,
window_length=paramreg.steps[i_step_str].window_length,
paramreg=Paramreg(step=paramreg.steps[i_step_str].step,
type=paramreg.steps[i_step_str].type,
algo='Translation',
metric=paramreg.steps[i_step_str].metric,
iter=paramreg.steps[i_step_str].iter,
shrink=paramreg.steps[i_step_str].shrink,
smooth=paramreg.steps[i_step_str].smooth,
gradStep=paramreg.steps[i_step_str].gradStep),
warp_forward_out=warp_forward_out,
warp_inverse_out=warp_inverse_out,
factor=param.outlier_factor,
verbose=param.verbose)
cmd = ('')
elif paramreg.steps[i_step_str].algo == 'slicereg2d_translation':
from msct_register import register_slicereg2d_translation
warp_forward_out = 'step' + i_step_str + 'Warp.nii.gz'
warp_inverse_out = 'step' + i_step_str + 'InverseWarp.nii.gz'
register_slicereg2d_translation(
src,
dest,
window_length=paramreg.steps[i_step_str].window_length,
paramreg=Paramreg(step=paramreg.steps[i_step_str].step,
type=paramreg.steps[i_step_str].type,
algo='Translation',
metric=paramreg.steps[i_step_str].metric,
iter=paramreg.steps[i_step_str].iter,
shrink=paramreg.steps[i_step_str].shrink,
smooth=paramreg.steps[i_step_str].smooth,
gradStep=paramreg.steps[i_step_str].gradStep),
fname_mask=fname_mask,
warp_forward_out=warp_forward_out,
warp_inverse_out=warp_inverse_out,
factor=param.outlier_factor,
remove_temp_files=param.remove_temp_files,
verbose=param.verbose,
ants_registration_params=ants_registration_params)
cmd = ('')
elif paramreg.steps[i_step_str].algo == 'slicereg2d_rigid':
from msct_register import register_slicereg2d_rigid
warp_forward_out = 'step' + i_step_str + 'Warp.nii.gz'
warp_inverse_out = 'step' + i_step_str + 'InverseWarp.nii.gz'
register_slicereg2d_rigid(
src,
dest,
window_length=paramreg.steps[i_step_str].window_length,
paramreg=Paramreg(step=paramreg.steps[i_step_str].step,
type=paramreg.steps[i_step_str].type,
algo='Rigid',
metric=paramreg.steps[i_step_str].metric,
iter=paramreg.steps[i_step_str].iter,
shrink=paramreg.steps[i_step_str].shrink,
smooth=paramreg.steps[i_step_str].smooth,
gradStep=paramreg.steps[i_step_str].gradStep),
fname_mask=fname_mask,
warp_forward_out=warp_forward_out,
warp_inverse_out=warp_inverse_out,
factor=param.outlier_factor,
remove_temp_files=param.remove_temp_files,
verbose=param.verbose,
ants_registration_params=ants_registration_params)
cmd = ('')
elif paramreg.steps[i_step_str].algo == 'slicereg2d_affine':
from msct_register import register_slicereg2d_affine
warp_forward_out = 'step' + i_step_str + 'Warp.nii.gz'
warp_inverse_out = 'step' + i_step_str + 'InverseWarp.nii.gz'
register_slicereg2d_affine(
src,
dest,
window_length=paramreg.steps[i_step_str].window_length,
paramreg=Paramreg(step=paramreg.steps[i_step_str].step,
type=paramreg.steps[i_step_str].type,
algo='Affine',
metric=paramreg.steps[i_step_str].metric,
iter=paramreg.steps[i_step_str].iter,
shrink=paramreg.steps[i_step_str].shrink,
smooth=paramreg.steps[i_step_str].smooth,
gradStep=paramreg.steps[i_step_str].gradStep),
fname_mask=fname_mask,
warp_forward_out=warp_forward_out,
warp_inverse_out=warp_inverse_out,
factor=param.outlier_factor,
remove_temp_files=param.remove_temp_files,
verbose=param.verbose,
ants_registration_params=ants_registration_params)
cmd = ('')
elif paramreg.steps[i_step_str].algo == 'slicereg2d_syn':
from msct_register import register_slicereg2d_syn
warp_forward_out = 'step' + i_step_str + 'Warp.nii.gz'
warp_inverse_out = 'step' + i_step_str + 'InverseWarp.nii.gz'
register_slicereg2d_syn(
src,
dest,
window_length=paramreg.steps[i_step_str].window_length,
paramreg=Paramreg(step=paramreg.steps[i_step_str].step,
type=paramreg.steps[i_step_str].type,
algo='SyN',
metric=paramreg.steps[i_step_str].metric,
iter=paramreg.steps[i_step_str].iter,
shrink=paramreg.steps[i_step_str].shrink,
smooth=paramreg.steps[i_step_str].smooth,
gradStep=paramreg.steps[i_step_str].gradStep),
fname_mask=fname_mask,
warp_forward_out=warp_forward_out,
warp_inverse_out=warp_inverse_out,
factor=param.outlier_factor,
remove_temp_files=param.remove_temp_files,
verbose=param.verbose,
ants_registration_params=ants_registration_params)
cmd = ('')
elif paramreg.steps[i_step_str].algo == 'slicereg2d_bsplinesyn':
from msct_register import register_slicereg2d_bsplinesyn
warp_forward_out = 'step' + i_step_str + 'Warp.nii.gz'
warp_inverse_out = 'step' + i_step_str + 'InverseWarp.nii.gz'
register_slicereg2d_bsplinesyn(
src,
dest,
window_length=paramreg.steps[i_step_str].window_length,
paramreg=Paramreg(step=paramreg.steps[i_step_str].step,
type=paramreg.steps[i_step_str].type,
algo='BSplineSyN',
metric=paramreg.steps[i_step_str].metric,
iter=paramreg.steps[i_step_str].iter,
shrink=paramreg.steps[i_step_str].shrink,
smooth=paramreg.steps[i_step_str].smooth,
gradStep=paramreg.steps[i_step_str].gradStep),
fname_mask=fname_mask,
warp_forward_out=warp_forward_out,
warp_inverse_out=warp_inverse_out,
factor=param.outlier_factor,
remove_temp_files=param.remove_temp_files,
verbose=param.verbose,
ants_registration_params=ants_registration_params)
cmd = ('')
elif paramreg.steps[i_step_str].algo.lower() in ants_registration_params:
from msct_image import pad_image
# Pad the destination image (because ants doesn't deform the extremities)
# N.B. no need to pad if iter = 0
if not paramreg.steps[i_step_str].iter == '0':
dest_pad = sct.add_suffix(dest, '_pad')
pad_image(dest, dest_pad, param.padding)
dest = dest_pad
cmd = (
'isct_antsRegistration '
'--dimensionality 3 '
'--transform ' + paramreg.steps[i_step_str].algo + '[' +
paramreg.steps[i_step_str].gradStep +
ants_registration_params[paramreg.steps[i_step_str].algo.lower()] +
'] '
'--metric ' + paramreg.steps[i_step_str].metric + '[' + dest +
',' + src + ',1,' + metricSize + '] '
'--convergence ' + paramreg.steps[i_step_str].iter + ' '
'--shrink-factors ' + paramreg.steps[i_step_str].shrink + ' '
'--smoothing-sigmas ' + paramreg.steps[i_step_str].smooth + 'mm '
'--restrict-deformation 1x1x0 '
'--output [step' + i_step_str + ',' + src + '_regStep' +
i_step_str + '.nii] '
'--interpolation BSpline[3] ' + masking)
if param.verbose >= 1:
cmd += ' --verbose 1'
if paramreg.steps[i_step_str].algo in ['rigid', 'affine']:
warp_forward_out = 'step' + i_step_str + '0GenericAffine.mat'
warp_inverse_out = '-step' + i_step_str + '0GenericAffine.mat'
else:
warp_forward_out = 'step' + i_step_str + '0Warp.nii.gz'
warp_inverse_out = 'step' + i_step_str + '0InverseWarp.nii.gz'
else:
sct.printv(
'\nERROR: algo ' + paramreg.steps[i_step_str].algo +
' does not exist. Exit program\n', 1, 'error')
# run registration
status, output = sct.run(cmd, param.verbose)
if os.path.isfile(warp_forward_out):
# rename warping fields
if paramreg.steps[i_step_str].algo in ['rigid', 'affine']:
warp_forward = 'warp_forward_' + i_step_str + '.mat'
os.rename(warp_forward_out, warp_forward)
warp_inverse = '-warp_forward_' + i_step_str + '.mat'
else:
warp_forward = 'warp_forward_' + i_step_str + '.nii.gz'
warp_inverse = 'warp_inverse_' + i_step_str + '.nii.gz'
os.rename(warp_forward_out, warp_forward)
os.rename(warp_inverse_out, warp_inverse)
else:
sct.printv(output, 1, 'error')
sct.printv('\nERROR: ANTs failed. Exit program.\n', 1, 'error')
return warp_forward, warp_inverse
def main(args=None):
# Initialization
# fname_anat = ''
# fname_centerline = ''
sigma = 3 # default value of the standard deviation for the Gaussian smoothing (in terms of number of voxels)
# remove_temp_files = param.remove_temp_files
# verbose = param.verbose
start_time = time.time()
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
fname_anat = arguments['-i']
fname_centerline = arguments['-s']
if '-smooth' in arguments:
sigma = arguments['-smooth']
if '-r' in arguments:
remove_temp_files = int(arguments['-r'])
if '-v' in arguments:
verbose = int(arguments['-v'])
# Display arguments
sct.printv('\nCheck input arguments...')
sct.printv(' Volume to smooth .................. ' + fname_anat)
sct.printv(' Centerline ........................ ' + fname_centerline)
sct.printv(' Sigma (mm) ........................ ' + str(sigma))
sct.printv(' Verbose ........................... ' + str(verbose))
# Check that input is 3D:
from msct_image import Image
nx, ny, nz, nt, px, py, pz, pt = Image(fname_anat).dim
dim = 4 # by default, will be adjusted later
if nt == 1:
dim = 3
if nz == 1:
dim = 2
if dim == 4:
sct.printv(
'WARNING: the input image is 4D, please split your image to 3D before smoothing spinalcord using :\n'
'sct_image -i ' + fname_anat + ' -split t -o ' + fname_anat,
verbose, 'warning')
sct.printv('4D images not supported, aborting ...', verbose, 'error')
# Extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
path_centerline, file_centerline, ext_centerline = sct.extract_fname(
fname_centerline)
# create temporary folder
sct.printv('\nCreate temporary folder...', verbose)
path_tmp = sct.slash_at_the_end('tmp.' + time.strftime("%y%m%d%H%M%S"), 1)
sct.run('mkdir ' + path_tmp, verbose)
# Copying input data to tmp folder
sct.printv('\nCopying input data to tmp folder and convert to nii...',
verbose)
sct.run('cp ' + fname_anat + ' ' + path_tmp + 'anat' + ext_anat, verbose)
sct.run(
'cp ' + fname_centerline + ' ' + path_tmp + 'centerline' +
ext_centerline, verbose)
# go to tmp folder
os.chdir(path_tmp)
# convert to nii format
convert('anat' + ext_anat, 'anat.nii')
convert('centerline' + ext_centerline, 'centerline.nii')
# Change orientation of the input image into RPI
sct.printv('\nOrient input volume to RPI orientation...')
fname_anat_rpi = set_orientation('anat.nii', 'RPI', filename=True)
move(fname_anat_rpi, 'anat_rpi.nii')
# Change orientation of the input image into RPI
sct.printv('\nOrient centerline to RPI orientation...')
fname_centerline_rpi = set_orientation('centerline.nii',
'RPI',
filename=True)
move(fname_centerline_rpi, 'centerline_rpi.nii')
# Straighten the spinal cord
# 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)
if os.path.isfile('../warp_curve2straight.nii.gz') and os.path.isfile(
'../warp_straight2curve.nii.gz') and os.path.isfile(
'../straight_ref.nii.gz'):
# if they exist, copy them into current folder
sct.printv(
'WARNING: Straightening was already run previously. Copying warping fields...',
verbose, 'warning')
shutil.copy('../warp_curve2straight.nii.gz',
'warp_curve2straight.nii.gz')
shutil.copy('../warp_straight2curve.nii.gz',
'warp_straight2curve.nii.gz')
shutil.copy('../straight_ref.nii.gz', 'straight_ref.nii.gz')
# apply straightening
sct.run(
'sct_apply_transfo -i anat_rpi.nii -w warp_curve2straight.nii.gz -d straight_ref.nii.gz -o anat_rpi_straight.nii -x spline',
verbose)
else:
sct.run(
'sct_straighten_spinalcord -i anat_rpi.nii -s centerline_rpi.nii -qc 0 -x spline',
verbose)
# Smooth the straightened image along z
sct.printv('\nSmooth the straightened image along z...')
sct.run(
'sct_maths -i anat_rpi_straight.nii -smooth 0,0,' + str(sigma) +
' -o anat_rpi_straight_smooth.nii', verbose)
# Apply the reversed warping field to get back the curved spinal cord
sct.printv(
'\nApply the reversed warping field to get back the curved spinal cord...'
)
sct.run(
'sct_apply_transfo -i anat_rpi_straight_smooth.nii -o anat_rpi_straight_smooth_curved.nii -d anat.nii -w warp_straight2curve.nii.gz -x spline',
verbose)
# replace zeroed voxels by original image (issue #937)
sct.printv('\nReplace zeroed voxels by original image...', verbose)
nii_smooth = Image('anat_rpi_straight_smooth_curved.nii')
data_smooth = nii_smooth.data
data_input = Image('anat.nii').data
indzero = np.where(data_smooth == 0)
data_smooth[indzero] = data_input[indzero]
nii_smooth.data = data_smooth
nii_smooth.setFileName('anat_rpi_straight_smooth_curved_nonzero.nii')
nii_smooth.save()
# come back to parent folder
os.chdir('..')
# Generate output file
sct.printv('\nGenerate output file...')
sct.generate_output_file(
path_tmp + '/anat_rpi_straight_smooth_curved_nonzero.nii',
file_anat + '_smooth' + ext_anat)
# Remove temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...')
sct.run('rm -rf ' + path_tmp)
# Display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(round(elapsed_time))) +
's\n')
# to view results
sct.printv('Done! To view results, type:', verbose)
sct.printv('fslview ' + file_anat + ' ' + file_anat + '_smooth &\n',
verbose, 'info')
def register(src, dest, paramreg, param, i_step_str):
# initiate default parameters of antsRegistration transformation
ants_registration_params = {
'rigid': '',
'affine': '',
'compositeaffine': '',
'similarity': '',
'translation': '',
'bspline': ',10',
'gaussiandisplacementfield': ',3,0',
'bsplinedisplacementfield': ',5,10',
'syn': ',3,0',
'bsplinesyn': ',1,3'
}
output = '' # default output if problem
# display arguments
sct.printv('Registration parameters:', param.verbose)
sct.printv(' type ........... ' + paramreg.steps[i_step_str].type,
param.verbose)
sct.printv(' algo ........... ' + paramreg.steps[i_step_str].algo,
param.verbose)
sct.printv(' slicewise ...... ' + paramreg.steps[i_step_str].slicewise,
param.verbose)
sct.printv(' metric ......... ' + paramreg.steps[i_step_str].metric,
param.verbose)
sct.printv(' iter ........... ' + paramreg.steps[i_step_str].iter,
param.verbose)
sct.printv(' smooth ......... ' + paramreg.steps[i_step_str].smooth,
param.verbose)
sct.printv(' laplacian ...... ' + paramreg.steps[i_step_str].laplacian,
param.verbose)
sct.printv(' shrink ......... ' + paramreg.steps[i_step_str].shrink,
param.verbose)
sct.printv(' gradStep ....... ' + paramreg.steps[i_step_str].gradStep,
param.verbose)
sct.printv(' deformation .... ' + paramreg.steps[i_step_str].deformation,
param.verbose)
sct.printv(' init ........... ' + paramreg.steps[i_step_str].init,
param.verbose)
sct.printv(' poly ........... ' + paramreg.steps[i_step_str].poly,
param.verbose)
sct.printv(' dof ............ ' + paramreg.steps[i_step_str].dof,
param.verbose)
sct.printv(' smoothWarpXY ... ' + paramreg.steps[i_step_str].smoothWarpXY,
param.verbose)
# set metricSize
if paramreg.steps[i_step_str].metric == 'MI':
metricSize = '32' # corresponds to number of bins
else:
metricSize = '4' # corresponds to radius (for CC, MeanSquares...)
# set masking
if param.fname_mask:
fname_mask = 'mask.nii.gz'
masking = '-x mask.nii.gz'
else:
fname_mask = ''
masking = ''
if paramreg.steps[i_step_str].algo == 'slicereg':
# check if user used type=label
if paramreg.steps[i_step_str].type == 'label':
sct.printv(
'\nERROR: this algo is not compatible with type=label. Please use type=im or type=seg',
1, 'error')
else:
from msct_image import find_zmin_zmax
# threshold images (otherwise, automatic crop does not work -- see issue #293)
src_th = sct.add_suffix(src, '_th')
from msct_image import Image
nii = Image(src)
data = nii.data
data[data < 0.1] = 0
nii.data = data
nii.setFileName(src_th)
nii.save()
# sct.run(fsloutput+'fslmaths '+src+' -thr 0.1 '+src_th, param.verbose)
dest_th = sct.add_suffix(dest, '_th')
nii = Image(dest)
data = nii.data
data[data < 0.1] = 0
nii.data = data
nii.setFileName(dest_th)
nii.save()
# sct.run(fsloutput+'fslmaths '+dest+' -thr 0.1 '+dest_th, param.verbose)
# find zmin and zmax
zmin_src, zmax_src = find_zmin_zmax(src_th)
zmin_dest, zmax_dest = find_zmin_zmax(dest_th)
zmin_total = max([zmin_src, zmin_dest])
zmax_total = min([zmax_src, zmax_dest])
# crop data
src_crop = sct.add_suffix(src, '_crop')
sct.run(
'sct_crop_image -i ' + src + ' -o ' + src_crop +
' -dim 2 -start ' + str(zmin_total) + ' -end ' +
str(zmax_total), param.verbose)
dest_crop = sct.add_suffix(dest, '_crop')
sct.run(
'sct_crop_image -i ' + dest + ' -o ' + dest_crop +
' -dim 2 -start ' + str(zmin_total) + ' -end ' +
str(zmax_total), param.verbose)
# update variables
src = src_crop
dest = dest_crop
scr_regStep = sct.add_suffix(src, '_regStep' + i_step_str)
# estimate transfo
cmd = (
'isct_antsSliceRegularizedRegistration '
'-t Translation[' + paramreg.steps[i_step_str].gradStep + '] '
'-m ' + paramreg.steps[i_step_str].metric + '[' + dest + ',' +
src + ',1,' + metricSize + ',Regular,0.2] '
'-p ' + paramreg.steps[i_step_str].poly + ' '
'-i ' + paramreg.steps[i_step_str].iter + ' '
'-f ' + paramreg.steps[i_step_str].shrink + ' '
'-s ' + paramreg.steps[i_step_str].smooth + ' '
'-v 1 ' # verbose (verbose=2 does not exist, so we force it to 1)
'-o [step' + i_step_str + ',' + scr_regStep +
'] ' # here the warp name is stage10 because antsSliceReg add "Warp"
+ masking)
warp_forward_out = 'step' + i_step_str + 'Warp.nii.gz'
warp_inverse_out = 'step' + i_step_str + 'InverseWarp.nii.gz'
# run command
status, output = sct.run(cmd, param.verbose)
# ANTS 3d
elif paramreg.steps[i_step_str].algo.lower(
) in ants_registration_params and paramreg.steps[
i_step_str].slicewise == '0':
# make sure type!=label. If type==label, this will be addressed later in the code.
if not paramreg.steps[i_step_str].type == 'label':
# Pad the destination image (because ants doesn't deform the extremities)
# N.B. no need to pad if iter = 0
if not paramreg.steps[i_step_str].iter == '0':
dest_pad = sct.add_suffix(dest, '_pad')
sct.run('sct_image -i ' + dest + ' -o ' + dest_pad +
' -pad 0,0,' + str(param.padding))
dest = dest_pad
# apply Laplacian filter
if not paramreg.steps[i_step_str].laplacian == '0':
sct.printv('\nApply Laplacian filter', param.verbose)
sct.run('sct_maths -i ' + src + ' -laplacian ' +
paramreg.steps[i_step_str].laplacian + ',' +
paramreg.steps[i_step_str].laplacian + ',0 -o ' +
sct.add_suffix(src, '_laplacian'))
sct.run('sct_maths -i ' + dest + ' -laplacian ' +
paramreg.steps[i_step_str].laplacian + ',' +
paramreg.steps[i_step_str].laplacian + ',0 -o ' +
sct.add_suffix(dest, '_laplacian'))
src = sct.add_suffix(src, '_laplacian')
dest = sct.add_suffix(dest, '_laplacian')
# Estimate transformation
sct.printv('\nEstimate transformation', param.verbose)
scr_regStep = sct.add_suffix(src, '_regStep' + i_step_str)
cmd = ('isct_antsRegistration '
'--dimensionality 3 '
'--transform ' + paramreg.steps[i_step_str].algo + '[' +
paramreg.steps[i_step_str].gradStep +
ants_registration_params[
paramreg.steps[i_step_str].algo.lower()] + '] '
'--metric ' + paramreg.steps[i_step_str].metric + '[' +
dest + ',' + src + ',1,' + metricSize + '] '
'--convergence ' + paramreg.steps[i_step_str].iter + ' '
'--shrink-factors ' + paramreg.steps[i_step_str].shrink +
' '
'--smoothing-sigmas ' + paramreg.steps[i_step_str].smooth +
'mm '
'--restrict-deformation ' +
paramreg.steps[i_step_str].deformation + ' '
'--output [step' + i_step_str + ',' + scr_regStep + '] '
'--interpolation BSpline[3] '
'--verbose 1 ' + masking)
# add init translation
if not paramreg.steps[i_step_str].init == '':
init_dict = {
'geometric': '0',
'centermass': '1',
'origin': '2'
}
cmd += ' -r [' + dest + ',' + src + ',' + init_dict[
paramreg.steps[i_step_str].init] + ']'
# run command
status, output = sct.run(cmd, param.verbose)
# get appropriate file name for transformation
if paramreg.steps[i_step_str].algo in [
'rigid', 'affine', 'translation'
]:
warp_forward_out = 'step' + i_step_str + '0GenericAffine.mat'
warp_inverse_out = '-step' + i_step_str + '0GenericAffine.mat'
else:
warp_forward_out = 'step' + i_step_str + '0Warp.nii.gz'
warp_inverse_out = 'step' + i_step_str + '0InverseWarp.nii.gz'
# ANTS 2d
elif paramreg.steps[i_step_str].algo.lower(
) in ants_registration_params and paramreg.steps[
i_step_str].slicewise == '1':
# make sure type!=label. If type==label, this will be addressed later in the code.
if not paramreg.steps[i_step_str].type == 'label':
from msct_register import register_slicewise
# if shrink!=1, force it to be 1 (otherwise, it generates a wrong 3d warping field). TODO: fix that!
if not paramreg.steps[i_step_str].shrink == '1':
sct.printv(
'\nWARNING: when using slicewise with SyN or BSplineSyN, shrink factor needs to be one. Forcing shrink=1.',
1, 'warning')
paramreg.steps[i_step_str].shrink = '1'
warp_forward_out = 'step' + i_step_str + 'Warp.nii.gz'
warp_inverse_out = 'step' + i_step_str + 'InverseWarp.nii.gz'
register_slicewise(
src,
dest,
paramreg=paramreg.steps[i_step_str],
fname_mask=fname_mask,
warp_forward_out=warp_forward_out,
warp_inverse_out=warp_inverse_out,
ants_registration_params=ants_registration_params,
path_qc=param.path_qc,
verbose=param.verbose)
# slice-wise transfo
elif paramreg.steps[i_step_str].algo in [
'centermass', 'centermassrot', 'columnwise'
]:
# if type=im, sends warning
if paramreg.steps[i_step_str].type == 'im':
sct.printv(
'\nWARNING: algo ' + paramreg.steps[i_step_str].algo +
' should be used with type=seg.\n', 1, 'warning')
# if type=label, exit with error
elif paramreg.steps[i_step_str].type == 'label':
sct.printv(
'\nERROR: this algo is not compatible with type=label. Please use type=im or type=seg',
1, 'error')
# check if user provided a mask-- if so, inform it will be ignored
if not fname_mask == '':
sct.printv(
'\nWARNING: algo ' + paramreg.steps[i_step_str].algo +
' will ignore the provided mask.\n', 1, 'warning')
# smooth data
if not paramreg.steps[i_step_str].smooth == '0':
sct.printv('\nSmooth data', param.verbose)
sct.run('sct_maths -i ' + src + ' -smooth ' +
paramreg.steps[i_step_str].smooth + ',' +
paramreg.steps[i_step_str].smooth + ',0 -o ' +
sct.add_suffix(src, '_smooth'))
sct.run('sct_maths -i ' + dest + ' -smooth ' +
paramreg.steps[i_step_str].smooth + ',' +
paramreg.steps[i_step_str].smooth + ',0 -o ' +
sct.add_suffix(dest, '_smooth'))
src = sct.add_suffix(src, '_smooth')
dest = sct.add_suffix(dest, '_smooth')
from msct_register import register_slicewise
warp_forward_out = 'step' + i_step_str + 'Warp.nii.gz'
warp_inverse_out = 'step' + i_step_str + 'InverseWarp.nii.gz'
register_slicewise(src,
dest,
paramreg=paramreg.steps[i_step_str],
fname_mask=fname_mask,
warp_forward_out=warp_forward_out,
warp_inverse_out=warp_inverse_out,
ants_registration_params=ants_registration_params,
path_qc=param.path_qc,
verbose=param.verbose)
else:
sct.printv(
'\nERROR: algo ' + paramreg.steps[i_step_str].algo +
' does not exist. Exit program\n', 1, 'error')
# landmark-based registration
if paramreg.steps[i_step_str].type in ['label']:
# check if user specified ilabel and dlabel
# TODO
warp_forward_out = 'step' + i_step_str + '0GenericAffine.txt'
warp_inverse_out = '-step' + i_step_str + '0GenericAffine.txt'
from msct_register_landmarks import register_landmarks
register_landmarks(src,
dest,
paramreg.steps[i_step_str].dof,
fname_affine=warp_forward_out,
verbose=param.verbose,
path_qc=param.path_qc)
if not os.path.isfile(warp_forward_out):
# no forward warping field for rigid and affine
sct.printv(
'\nERROR: file ' + warp_forward_out +
' doesn\'t exist (or is not a file).\n' + output +
'\nERROR: ANTs failed. Exit program.\n', 1, 'error')
elif not os.path.isfile(
warp_inverse_out) and paramreg.steps[i_step_str].algo not in [
'rigid', 'affine', 'translation'
] and paramreg.steps[i_step_str].type not in ['label']:
# no inverse warping field for rigid and affine
sct.printv(
'\nERROR: file ' + warp_inverse_out +
' doesn\'t exist (or is not a file).\n' + output +
'\nERROR: ANTs failed. Exit program.\n', 1, 'error')
else:
# rename warping fields
if (paramreg.steps[i_step_str].algo.lower()
in ['rigid', 'affine', 'translation']
and paramreg.steps[i_step_str].slicewise == '0'):
# if ANTs is used with affine/rigid --> outputs .mat file
warp_forward = 'warp_forward_' + i_step_str + '.mat'
os.rename(warp_forward_out, warp_forward)
warp_inverse = '-warp_forward_' + i_step_str + '.mat'
elif paramreg.steps[i_step_str].type in ['label']:
# if label-based registration is used --> outputs .txt file
warp_forward = 'warp_forward_' + i_step_str + '.txt'
os.rename(warp_forward_out, warp_forward)
warp_inverse = '-warp_forward_' + i_step_str + '.txt'
else:
warp_forward = 'warp_forward_' + i_step_str + '.nii.gz'
warp_inverse = 'warp_inverse_' + i_step_str + '.nii.gz'
os.rename(warp_forward_out, warp_forward)
os.rename(warp_inverse_out, warp_inverse)
return warp_forward, warp_inverse
#!/usr/bin/env python
# change type of template data
import os
import commands
import sys
from shutil import move
# Get path of the toolbox
status, path_sct = commands.getstatusoutput('echo $SCT_DIR')
# Append path that contains scripts, to be able to load modules
sys.path.append(path_sct + '/scripts')
from msct_image import Image
import sct_utils as sct
path_template = '/Users/julien/data/PAM50/template'
folder_PAM50 = 'PAM50/template/'
os.chdir(path_template)
sct.create_folder(folder_PAM50)
for file_template in ['MNI-Poly-AMU_T1.nii.gz', 'MNI-Poly-AMU_T2.nii.gz', 'MNI-Poly-AMU_T2star.nii.gz']:
im = Image(file_template)
# remove negative values
data = im.data
data[data<0] = 0
im.data = data
im.changeType('uint16')
file_new = file_template.replace('MNI-Poly-AMU', 'PAM50')
im.setFileName(file_new)
im.save()
# move to folder
move(file_new, folder_PAM50+file_new)
def wm_registration(param, smooth=False, init=False):
path, fixed_name, ext = sct.extract_fname(param.fname_fixed)
path_moving, moving_name, ext = sct.extract_fname(param.fname_moving)
path, fixed_seg_name, ext = sct.extract_fname(param.fname_seg_fixed)
path_moving_seg, moving_seg_name, ext = sct.extract_fname(param.fname_seg_moving)
# cropping in x & y directions
fixed_name_temp = fixed_name + "_crop"
cmd = "sct_crop_image -i " + fixed_name + ext + " -o " + fixed_name_temp + ext + " -m " + fixed_seg_name + ext + " -shift 5,5 -dim 0,1"
sct.run(cmd)
fixed_name = fixed_name_temp
fixed_seg_name_temp = fixed_seg_name+"_crop"
sct.run("sct_crop_image -i " + fixed_seg_name + ext + " -o " + fixed_seg_name_temp + ext + " -m " + fixed_seg_name + ext + " -shift 5,5 -dim 0,1")
fixed_seg_name = fixed_seg_name_temp
# padding the images
moving_name_pad = moving_name+"_pad"
fixed_name_pad = fixed_name+"_pad"
sct.run('sct_maths -i '+path_moving+moving_name+ext+' -o '+moving_name_pad+ext+' -pad 0,0,'+str(param.padding))
sct.run('sct_maths -i '+fixed_name+ext+' -o '+fixed_name_pad+ext+' -pad 0,0,'+str(param.padding))
moving_name = moving_name_pad
fixed_name = fixed_name_pad
moving_seg_name_pad = moving_seg_name+"_pad"
sct.run('sct_maths -i '+path_moving_seg+moving_seg_name+ext+' -o '+moving_seg_name_pad+ext+' -pad 0,0,'+str(param.padding))
moving_seg_name = moving_seg_name_pad
fixed_seg_name_pad = fixed_seg_name+"_pad"
sct.run('sct_maths -i '+fixed_seg_name+ext+' -o '+fixed_seg_name_pad+ext+' -pad 0,0,'+str(param.padding))
fixed_seg_name = fixed_seg_name_pad
if param.metric == 'MeanSquares':
# offset
old_min = 0
old_max = 1
new_min = 100
new_max = 200
fixed_im = Image(fixed_name+ext)
fixed_im.data = (fixed_im.data - old_min)*(new_max - new_min)/(old_max - old_min) + new_min
fixed_im.save()
moving_im = Image(moving_name+ext)
moving_im.data = (moving_im.data - old_min)*(new_max - new_min)/(old_max - old_min) + new_min
moving_im.save()
if smooth:
# smoothing the images to register
sct.printv('\nSmoothing the images...', verbose, 'normal')
fixed_smooth = fixed_name+'_smooth'
moving_smooth = moving_name+'_smooth'
sct.run('sct_maths -i '+fixed_name+ext+' -smooth 1 -o '+fixed_smooth+ext)
sct.run('sct_maths -i '+moving_name+ext+' -smooth 1 -o '+moving_smooth+ext)
fixed_name = fixed_smooth
moving_name = moving_smooth
# registration of the gray matter
sct.printv('\nDeforming the image...', verbose, 'normal')
moving_name_reg = moving_name+"_deformed"
param_multimodal_reg = 'step=1,type=seg,algo=slicereg,metric=MeanSquares:step=2,type=im,algo=bsplinesyn,metric=MeanSquares,iter=5,shrink=2'
if param.transformation == 'BSplineSyN':
transfo_params = ',3,0'
elif param.transformation == 'SyN': # SyN gives bad results...
transfo_params = ',1,1'
else:
transfo_params = ''
cmd = 'sct_register_multimodal -i '+moving_name+ext+' -d '+fixed_name+ext+' -iseg '+moving_seg_name+ext+' -dseg '+fixed_seg_name+ext+' -p '+param_multimodal_reg+' -o '+moving_name_reg
ext_multimodal = '.nii'
'''
cmd = 'isct_antsRegistration --dimensionality 3 --interpolation '+param.interpolation+' --transform '+param.transformation+'['+param.gradient_step+transfo_params+'] --metric '+param.metric+'['+fixed_name+ext+','+moving_name+ext+',1,4] --output ['+moving_name_reg+','+moving_name_reg+ext+'] --convergence '+param.iteration+' --shrink-factors 1x1 --smoothing-sigmas 0x0 '
if init:
# initialize the transformation using the intensity
cmd += ' -r ['+fixed_name+ext+','+moving_name+ext+',1] '
cmd += " --masks ["+fixed_seg_name+ext+","+moving_seg_name + ext + "]"
# cmd += " -m ["+fixed_seg_name+".nii,"+moving_seg_name+".nii]"
'''
sct.run(cmd)
if init:
# transform the .mat warping field into a nifti file:
warp0_mat = moving_name_reg+'0GenericAffine.mat'
warp0, warp0_inv = transform_mat_to_warp(warp0_mat, moving_name_reg+ext, fixed_name+ext)
if param.transformation == 'Affine':
warp_tot = warp0
inverse_warp_tot = warp0_inv
else:
warp1 = moving_name_reg+'1Warp.nii.gz'
warp1_inv = moving_name_reg+'1InverseWarp.nii.gz'
# Concatenate the warping fields from the 2 steps into a warp total and an inverse
warp_tot = moving_name_reg+'_warp_tot'+ext
inverse_warp_tot = moving_name_reg+'_inverse_warp_tot'+ext
sct.run('sct_concat_transfo -w '+warp0+','+warp1+' -d '+fixed_name+ext+' -o '+warp_tot)
sct.run('sct_concat_transfo -w '+warp1_inv+','+warp0_inv+' -d '+moving_name+ext+' -o '+inverse_warp_tot)
else:
warp_tot = moving_name_reg+'0Warp.nii.gz'
inverse_warp_tot = moving_name_reg+'0InverseWarp.nii.gz'
if param.metric == 'MeanSquares':
# removing offset
fixed_im = Image(fixed_name+ext)
fixed_im.data = (fixed_im.data - new_min)*(old_max - old_min)/(new_max - new_min) + old_min
fixed_im.save()
moving_im = Image(moving_name_reg+ext_multimodal)
moving_im.data = (moving_im.data - new_min)*(old_max - old_min)/(new_max - new_min) + old_min
moving_im.save()
# un-padding the images
moving_name_unpad = moving_name_reg+"_unpadded"
fixed_name_unpad = fixed_name+"_unpadded"
sct.run("sct_crop_image -i "+moving_name_reg+ext+" -dim 2 -start "+str(int(param.padding))+" -end -"+param.padding+" -o "+moving_name_unpad+ext)
sct.run("sct_crop_image -i "+fixed_name+ext+" -dim 2 -start "+str(int(param.padding))+" -end -"+param.padding+" -o "+fixed_name_unpad+ext)
'''
# warp_tot = moving_name_reg+"0Warp.nii.gz"
# inverse_warp_tot = moving_name_reg+"0InverseWarp.nii.gz"
warp_unpad = sct.add_suffix(warp_tot, '_unpad') # moving_name_reg+"0Warp_unpad.nii.gz"
inverse_warp_unpad = sct.add_suffix(inverse_warp_tot, '_unpad') # moving_name_reg+"0InverseWarp_unpad.nii.gz"
sct.run("sct_crop_image -i "+warp_tot+" -dim 2 -start "+str(int(param.padding))+" -end -"+param.padding+" -o "+warp_unpad)
sct.run("sct_crop_image -i "+inverse_warp_tot+" -dim 2 -start "+str(int(param.padding))+" -end -"+param.padding+" -o "+inverse_warp_unpad)
'''
path_output, file_output, ext_output = sct.extract_fname(param.fname_output)
warp_output = file_output+"_Warp"+ext_output
inverse_warp_output = file_output+"_InverseWarp"+ext_output
# with multimodal_reg
sct.run("mv warp_"+moving_name+"2"+fixed_name+".nii.gz "+warp_output)
sct.run("mv warp_"+fixed_name+"2"+moving_name+".nii.gz "+inverse_warp_output)
'''
# with antsReg
sct.run("mv "+warp_unpad+" "+warp_output)
sct.run("mv "+inverse_warp_unpad+" "+inverse_warp_output)
'''
moving_name_out = file_output+ext_output
# put the result and the reference in the same space using a registration with ANTs with no iteration:
sct.run('isct_antsRegistration -d 3 -t Translation[0] -m MI['+fixed_name_unpad+ext+','+moving_name_unpad+ext+',1,16] -o [regAffine,'+moving_name_out+'] -n BSpline[3] -c 0 -f 1 -s 0')
return warp_output, inverse_warp_output
def dmri_moco(param):
file_data = 'dmri'
ext_data = '.nii'
file_b0 = 'b0'
file_dwi = 'dwi'
mat_final = 'mat_final/'
file_dwi_group = 'dwi_averaged_groups' # no extension
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
ext_mat = 'Warp.nii.gz' # warping field
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(file_data + '.nii').dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz),
param.verbose)
# Identify b=0 and DWI images
sct.printv('\nIdentify b=0 and DWI images...', param.verbose)
index_b0, index_dwi, nb_b0, nb_dwi = identify_b0('bvecs.txt',
param.fname_bvals,
param.bval_min,
param.verbose)
# check if dmri and bvecs are the same size
if not nb_b0 + nb_dwi == nt:
sct.printv(
'\nERROR in ' + os.path.basename(__file__) + ': Size of data (' +
str(nt) + ') and size of bvecs (' + str(nb_b0 + nb_dwi) +
') are not the same. Check your bvecs file.\n', 1, 'error')
sys.exit(2)
# Prepare NIFTI (mean/groups...)
#===================================================================================================================
# Split into T dimension
sct.printv('\nSplit along T dimension...', param.verbose)
status, output = sct.run(
'sct_split_data -i ' + file_data + ext_data + ' -dim t -suffix _T',
param.verbose)
# Merge b=0 images
sct.printv('\nMerge b=0...', param.verbose)
# cmd = fsloutput + 'fslmerge -t ' + file_b0
# for it in range(nb_b0):
# cmd = cmd + ' ' + file_data + '_T' + str(index_b0[it]).zfill(4)
cmd = 'sct_concat_data -dim t -o ' + file_b0 + ext_data + ' -i '
for it in range(nb_b0):
cmd = cmd + file_data + '_T' + str(
index_b0[it]).zfill(4) + ext_data + ','
cmd = cmd[:-1] # remove ',' at the end of the string
status, output = sct.run(cmd, param.verbose)
sct.printv((' File created: ' + file_b0), param.verbose)
# Average b=0 images
sct.printv('\nAverage b=0...', param.verbose)
file_b0_mean = file_b0 + '_mean'
sct.run(
'sct_maths -i ' + file_b0 + '.nii' + ' -o ' + file_b0_mean + '.nii' +
' -mean t', param.verbose)
# if not average_data_across_dimension(file_b0+'.nii', file_b0_mean+'.nii', 3):
# sct.printv('ERROR in average_data_across_dimension', 1, 'error')
# cmd = fsloutput + 'fslmaths ' + file_b0 + ' -Tmean ' + file_b0_mean
# status, output = sct.run(cmd, param.verbose)
# Number of DWI groups
nb_groups = int(math.floor(nb_dwi / param.group_size))
# Generate groups indexes
group_indexes = []
for iGroup in range(nb_groups):
group_indexes.append(index_dwi[(iGroup *
param.group_size):((iGroup + 1) *
param.group_size)])
# add the remaining images to the last DWI group
nb_remaining = nb_dwi % param.group_size # number of remaining images
if nb_remaining > 0:
nb_groups += 1
group_indexes.append(index_dwi[len(index_dwi) -
nb_remaining:len(index_dwi)])
# DWI groups
for iGroup in range(nb_groups):
sct.printv('\nDWI group: ' + str((iGroup + 1)) + '/' + str(nb_groups),
param.verbose)
# get index
index_dwi_i = group_indexes[iGroup]
nb_dwi_i = len(index_dwi_i)
# Merge DW Images
sct.printv('Merge DW images...', param.verbose)
file_dwi_merge_i = file_dwi + '_' + str(iGroup)
cmd = 'sct_concat_data -dim t -o ' + file_dwi_merge_i + ext_data + ' -i '
for it in range(nb_dwi_i):
cmd = cmd + file_data + '_T' + str(
index_dwi_i[it]).zfill(4) + ext_data + ','
cmd = cmd[:-1] # remove ',' at the end of the string
sct.run(cmd, param.verbose)
# cmd = fsloutput + 'fslmerge -t ' + file_dwi_merge_i
# for it in range(nb_dwi_i):
# cmd = cmd +' ' + file_data + '_T' + str(index_dwi_i[it]).zfill(4)
# Average DW Images
sct.printv('Average DW images...', param.verbose)
file_dwi_mean = file_dwi + '_mean_' + str(iGroup)
sct.run(
'sct_maths -i ' + file_dwi_merge_i + '.nii' + ' -o ' +
file_dwi_mean + '.nii' + ' -mean t', param.verbose)
# if not average_data_across_dimension(file_dwi_merge_i+'.nii', file_dwi_mean+'.nii', 3):
# sct.printv('ERROR in average_data_across_dimension', 1, 'error')
# cmd = fsloutput + 'fslmaths ' + file_dwi_merge_i + ' -Tmean ' + file_dwi_mean
# sct.run(cmd, param.verbose)
# Merge DWI groups means
sct.printv('\nMerging DW files...', param.verbose)
# file_dwi_groups_means_merge = 'dwi_averaged_groups'
cmd = 'sct_concat_data -dim t -o ' + file_dwi_group + ext_data + ' -i '
for iGroup in range(nb_groups):
cmd = cmd + file_dwi + '_mean_' + str(iGroup) + ext_data + ','
cmd = cmd[:-1] # remove ',' at the end of the string
sct.run(cmd, param.verbose)
# cmd = fsloutput + 'fslmerge -t ' + file_dwi_group
# for iGroup in range(nb_groups):
# cmd = cmd + ' ' + file_dwi + '_mean_' + str(iGroup)
# Average DW Images
# TODO: USEFULL ???
sct.printv('\nAveraging all DW images...', param.verbose)
fname_dwi_mean = 'dwi_mean'
sct.run(
'sct_maths -i ' + file_dwi_group + '.nii' + ' -o ' + file_dwi_group +
'_mean.nii' + ' -mean t', param.verbose)
# if not average_data_across_dimension(file_dwi_group+'.nii', file_dwi_group+'_mean.nii', 3):
# sct.printv('ERROR in average_data_across_dimension', 1, 'error')
# sct.run(fsloutput + 'fslmaths ' + file_dwi_group + ' -Tmean ' + file_dwi_group+'_mean', param.verbose)
# segment dwi images using otsu algorithm
if param.otsu:
sct.printv('\nSegment group DWI using OTSU algorithm...',
param.verbose)
# import module
otsu = importlib.import_module('sct_otsu')
# get class from module
param_otsu = otsu.param() #getattr(otsu, param)
param_otsu.fname_data = file_dwi_group + '.nii'
param_otsu.threshold = param.otsu
param_otsu.file_suffix = '_seg'
# run otsu
otsu.otsu(param_otsu)
file_dwi_group = file_dwi_group + '_seg'
# extract first DWI volume as target for registration
nii = Image(file_dwi_group + '.nii')
data_crop = nii.data[:, :, :, index_dwi[0]:index_dwi[0] + 1]
nii.data = data_crop
nii.setFileName('target_dwi.nii')
nii.save()
# START MOCO
#===================================================================================================================
# Estimate moco on b0 groups
sct.printv(
'\n-------------------------------------------------------------------------------',
param.verbose)
sct.printv(' Estimating motion on b=0 images...', param.verbose)
sct.printv(
'-------------------------------------------------------------------------------',
param.verbose)
param_moco = param
param_moco.file_data = 'b0'
if index_dwi[0] != 0:
# If first DWI is not the first volume (most common), then there is a least one b=0 image before. In that case
# select it as the target image for registration of all b=0
param_moco.file_target = file_data + '_T' + str(
index_b0[index_dwi[0] - 1]).zfill(4)
else:
# If first DWI is the first volume, then the target b=0 is the first b=0 from the index_b0.
param_moco.file_target = file_data + '_T' + str(index_b0[0]).zfill(4)
param_moco.path_out = ''
param_moco.todo = 'estimate'
param_moco.mat_moco = 'mat_b0groups'
moco.moco(param_moco)
# Estimate moco on dwi groups
sct.printv(
'\n-------------------------------------------------------------------------------',
param.verbose)
sct.printv(' Estimating motion on DW images...', param.verbose)
sct.printv(
'-------------------------------------------------------------------------------',
param.verbose)
param_moco.file_data = file_dwi_group
param_moco.file_target = 'target_dwi' # target is the first DW image (closest to the first b=0)
param_moco.path_out = ''
#param_moco.todo = 'estimate'
param_moco.todo = 'estimate_and_apply'
param_moco.mat_moco = 'mat_dwigroups'
moco.moco(param_moco)
# create final mat folder
sct.create_folder(mat_final)
# Copy b=0 registration matrices
sct.printv('\nCopy b=0 registration matrices...', param.verbose)
for it in range(nb_b0):
sct.run(
'cp ' + 'mat_b0groups/' + 'mat.T' + str(it) + ext_mat + ' ' +
mat_final + 'mat.T' + str(index_b0[it]) + ext_mat, param.verbose)
# Copy DWI registration matrices
sct.printv('\nCopy DWI registration matrices...', param.verbose)
for iGroup in range(nb_groups):
for dwi in range(len(group_indexes[iGroup])):
sct.run(
'cp ' + 'mat_dwigroups/' + 'mat.T' + str(iGroup) + ext_mat +
' ' + mat_final + 'mat.T' + str(group_indexes[iGroup][dwi]) +
ext_mat, param.verbose)
# Spline Regularization along T
if param.spline_fitting:
moco.spline(mat_final, nt, nz, param.verbose, np.array(index_b0),
param.plot_graph)
# combine Eddy Matrices
if param.run_eddy:
param.mat_2_combine = 'mat_eddy'
param.mat_final = mat_final
moco.combine_matrix(param)
# Apply moco on all dmri data
sct.printv(
'\n-------------------------------------------------------------------------------',
param.verbose)
sct.printv(' Apply moco', param.verbose)
sct.printv(
'-------------------------------------------------------------------------------',
param.verbose)
param_moco.file_data = 'dmri'
param_moco.file_target = file_dwi + '_mean_' + str(
0) # reference for reslicing into proper coordinate system
param_moco.path_out = ''
param_moco.mat_moco = mat_final
param_moco.todo = 'apply'
moco.moco(param_moco)
# copy geometric information from header
# NB: this is required because WarpImageMultiTransform in 2D mode wrongly sets pixdim(3) to "1".
copy_header('dmri.nii', 'dmri_moco.nii')
# generate b0_moco_mean and dwi_moco_mean
cmd = 'sct_dmri_separate_b0_and_dwi -i dmri' + param.suffix + '.nii -b bvecs.txt -a 1'
if not param.fname_bvals == '':
cmd = cmd + ' -m ' + param.fname_bvals
sct.run(cmd, param.verbose)
def wm_registration(param):
path, fixed_name, ext = sct.extract_fname(param.fname_fixed)
path_moving, moving_name, ext = sct.extract_fname(param.fname_moving)
path, fixed_seg_name, ext = sct.extract_fname(param.fname_seg_fixed)
path_moving_seg, moving_seg_name, ext = sct.extract_fname(param.fname_seg_moving)
# cropping in x & y directions
fixed_name_temp = fixed_name + "_crop"
cmd = "sct_crop_image -i " + fixed_name + ext + " -o " + fixed_name_temp + ext + " -m " + fixed_seg_name + ext + " -shift 10,10 -dim 0,1"
sct.run(cmd)
fixed_name = fixed_name_temp
fixed_seg_name_temp = fixed_name+"_crop"
sct.run("sct_crop_image -i " + fixed_seg_name + ext + " -o " + fixed_seg_name_temp + ext + " -m " + fixed_seg_name + ext + " -shift 10,10 -dim 0,1")
fixed_seg_name = fixed_seg_name_temp
# padding the images
moving_name_pad = moving_name+"_pad"
fixed_name_pad = fixed_name+"_pad"
sct.run("c3d "+path_moving+moving_name+ext+" -pad 0x0x"+param.padding+"vox 0x0x"+param.padding+"vox 0 -o "+moving_name_pad+ext)
sct.run("c3d "+fixed_name+ext+" -pad 0x0x"+param.padding+"vox 0x0x"+param.padding+"vox 0 -o "+fixed_name_pad+ext)
moving_name = moving_name_pad
fixed_name = fixed_name_pad
moving_seg_name_pad = moving_seg_name+"_pad"
sct.run("c3d "+path_moving_seg+moving_seg_name+ext+" -pad 0x0x"+param.padding+"vox 0x0x"+param.padding+"vox 0 -o "+moving_seg_name_pad+ext)
moving_seg_name = moving_seg_name_pad
fixed_seg_name_pad = fixed_seg_name+"_pad"
sct.run("c3d "+fixed_seg_name+ext+" -pad 0x0x"+param.padding+"vox 0x0x"+param.padding+"vox 0 -o "+fixed_seg_name_pad+ext)
fixed_seg_name = fixed_seg_name_pad
# offset
old_min = 0
old_max = 1
new_min = 100
new_max = 200
fixed_im = Image(fixed_name+ext)
fixed_im.data = (fixed_im.data - old_min)*(new_max - new_min)/(old_max - old_min) + new_min
fixed_im.save()
moving_im = Image(moving_name+ext)
moving_im.data = (moving_im.data - old_min)*(new_max - new_min)/(old_max - old_min) + new_min
moving_im.save()
# registration of the gray matter
sct.printv('\nDeforming the image...', reg_param.verbose, 'normal')
moving_name_reg = moving_name+"_deformed"
if param.transformation == 'BSplineSyN':
transfo_params = ',3,0'
elif param.transforlation == 'SyN': # SyN gives bad results...
transfo_params = ',1,1'
else:
transfo_params = ''
cmd = 'isct_antsRegistration --dimensionality 3 --interpolation '+param.interpolation+' --transform '+param.transformation+'['+param.gradient_step+transfo_params+'] --metric '+param.metric+'['+fixed_name+ext+','+moving_name+ext+',1,4] --output ['+moving_name_reg+','+moving_name_reg+ext+'] --convergence '+param.iteration+' --shrink-factors 2x1 --smoothing-sigmas 0x0 '
cmd += " --masks ["+fixed_seg_name+ext+","+moving_seg_name + ext + "]"
# cmd += " -m ["+fixed_seg_name+".nii,"+moving_seg_name+".nii]"
sct.run(cmd)
moving_name = moving_name_reg
# removing offset
fixed_im = Image(fixed_name+ext)
fixed_im.data = (fixed_im.data - new_min)*(old_max - old_min)/(new_max - new_min) + old_min
fixed_im.save()
moving_im = Image(moving_name+ext)
moving_im.data = (moving_im.data - new_min)*(old_max - old_min)/(new_max - new_min) + old_min
moving_im.save()
# un-padding the images
moving_name_unpad = moving_name+"_unpadded"
sct.run("sct_crop_image -i "+moving_name+ext+" -dim 2 -start "+str(int(param.padding)-1)+" -end -"+param.padding+" -o "+moving_name_unpad+ext)
path_output, file_output, ext_output = sct.extract_fname(param.fname_output)
warp_output = file_output+"0Warp"+ext_output
inverse_warp_output = file_output+"0InverseWarp"+ext_output
sct.run("mv "+moving_name+"0Warp.nii.gz "+warp_output)
sct.run("mv "+moving_name+"0InverseWarp.nii.gz "+inverse_warp_output)
moving_name = moving_name_unpad
moving_name_out = file_output+ext_output
sct.run("c3d "+fixed_name+ext+" "+moving_name+ext+" -reslice-identity -o "+moving_name_out+ext)
return warp_output, inverse_warp_output
def create_mask():
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
# 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 = param.file_prefix+file_data+ext_data
# create temporary folder
sct.printv('\nCreate temporary folder...', param.verbose)
path_tmp = sct.tmp_create(param.verbose)
# )sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
# sct.run('mkdir '+path_tmp, param.verbose)
sct.printv('\nCheck orientation...', param.verbose)
orientation_input = get_orientation(Image(param.fname_data))
sct.printv('.. '+orientation_input, param.verbose)
reorient_coordinates = False
# copy input data to tmp folder
convert(param.fname_data, path_tmp+'data.nii')
if method_type == 'centerline':
convert(method_val, path_tmp+'centerline.nii.gz')
if method_type == 'point':
convert(method_val, path_tmp+'point.nii.gz')
# go to tmp folder
os.chdir(path_tmp)
# reorient to RPI
sct.printv('\nReorient to RPI...', param.verbose)
# if not orientation_input == 'RPI':
sct.run('sct_image -i data.nii -o data_RPI.nii -setorient RPI -v 0', verbose=False)
if method_type == 'centerline':
sct.run('sct_image -i centerline.nii.gz -o centerline_RPI.nii.gz -setorient RPI -v 0', verbose=False)
if method_type == 'point':
sct.run('sct_image -i point.nii.gz -o point_RPI.nii.gz -setorient RPI -v 0', verbose=False)
#
# if method_type == 'centerline':
# orientation_centerline = get_orientation_3d(method_val, filename=True)
# if not orientation_centerline == 'RPI':
# sct.run('sct_image -i ' + method_val + ' -o ' + path_tmp + 'centerline.nii.gz' + ' -setorient RPI -v 0', verbose=False)
# else:
# convert(method_val, path_tmp+'centerline.nii.gz')
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = Image('data_RPI.nii').dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz)+ ' x ' + str(nt), 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 3D.', param.verbose, 'warning')
# extract first volume to have 3d reference
nii = Image('data_RPI.nii')
data3d = nii.data[:,:,:,0]
nii.data = data3d
nii.save()
if method_type == 'coord':
# parse to get coordinate
coord = map(int, method_val.split('x'))
if method_type == 'point':
# get file name
fname_point = method_val
# 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.gz -display', param.verbose)
# parse to get coordinate
coord = output[output.find('Position=')+10:-17].split(',')
if method_type == 'center':
# set coordinate at center of FOV
coord = round(float(nx)/2), round(float(ny)/2)
if method_type == 'centerline':
# get name of centerline from user argument
fname_centerline = 'centerline_RPI.nii.gz'
else:
# generate volume with line along Z at coordinates 'coord'
sct.printv('\nCreate line...', param.verbose)
fname_centerline = create_line('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
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(numpy.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 = numpy.array([cx[iz], cy[iz]])
mask2d = create_mask2d(center, param.shape, param.size, nx, ny, even=param.even, spacing=spacing)
# Write NIFTI volumes
img = nibabel.Nifti1Image(mask2d, None, hdr)
nibabel.save(img, (file_mask+str(iz)+'.nii'))
# merge along Z
# cmd = 'fslmerge -z mask '
# CHANGE THAT CAN IMPACT SPEED:
# related to issue #755, we cannot open more than 256 files at one time.
# to solve this issue, we do not open more than 100 files
'''
im_list = []
im_temp = []
for iz in range(nz_not_null):
if iz != 0 and iz % 100 == 0:
im_temp.append(concat_data(im_list, 2))
im_list = [Image(file_mask + str(iz) + '.nii')]
else:
im_list.append(Image(file_mask+str(iz)+'.nii'))
if im_temp:
im_temp.append(concat_data(im_list, 2))
im_out = concat_data(im_temp, 2, no_expand=True)
else:
im_out = concat_data(im_list, 2)
'''
fname_list = [file_mask + str(iz) + '.nii' for iz in range(nz)]
im_out = concat_data(fname_list, dim=2)
im_out.setFileName('mask_RPI.nii.gz')
im_out.save()
# reorient if necessary
# if not orientation_input == 'RPI':
sct.run('sct_image -i mask_RPI.nii.gz -o mask.nii.gz -setorient ' + orientation_input, param.verbose)
# copy header input --> mask
im_dat = Image('data.nii')
im_mask = Image('mask.nii.gz')
im_mask = copy_header(im_dat, im_mask)
im_mask.save()
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', param.verbose)
sct.generate_output_file(path_tmp+'mask.nii.gz', param.fname_out)
# Remove temporary files
if param.remove_tmp_files == 1:
sct.printv('\nRemove temporary files...', param.verbose)
sct.run('rm -rf '+path_tmp, param.verbose, error_exit='warning')
# to view results
sct.printv('\nDone! To view results, type:', param.verbose)
sct.printv('fslview '+param.fname_data+' '+param.fname_out+' -l Red -t 0.5 &', param.verbose, 'info')
print
def execute(self):
print 'Execution of the SCAD algorithm in ' + str(os.getcwd())
original_name = self.input_image.file_name
vesselness_file_name = "imageVesselNessFilter.nii.gz"
raw_file_name = "raw.nii"
self.setup_debug_folder()
if self.debug:
import matplotlib.pyplot as plt # import for debug purposes
# create tmp and copy input
path_tmp = self.create_temporary_path()
conv.convert(self.input_image.absolutepath, path_tmp + raw_file_name)
if self.vesselness_provided:
sct.run('cp ' + vesselness_file_name + ' ' + path_tmp +
vesselness_file_name)
os.chdir(path_tmp)
# get input image information
img = Image(raw_file_name)
# save original orientation and change image to RPI
self.raw_orientation = img.change_orientation()
# get body symmetry
if self.enable_symmetry:
from msct_image import change_data_orientation
sym = SymmetryDetector(raw_file_name, self.contrast, crop_xy=0)
self.raw_symmetry = sym.execute()
img.change_orientation(self.raw_orientation)
self.output_debug_file(img, self.raw_symmetry, "body_symmetry")
img.change_orientation()
# vesselness filter
if not self.vesselness_provided:
sct.run('isct_vesselness -i ' + raw_file_name + ' -t ' +
self._contrast + " -radius " + str(self.spinalcord_radius))
# load vesselness filter data and perform minimum path on it
img = Image(vesselness_file_name)
self.output_debug_file(img, img.data, "Vesselness_Filter")
img.change_orientation()
self.minimum_path_data, self.J1_min_path, self.J2_min_path = get_minimum_path(
img.data, invert=1, debug=1)
self.output_debug_file(img, self.minimum_path_data, "minimal_path")
self.output_debug_file(img, self.J1_min_path, "J1_minimal_path")
self.output_debug_file(img, self.J2_min_path, "J2_minimal_path")
# Apply an exponent to the minimum path
self.minimum_path_powered = np.power(self.minimum_path_data,
self.minimum_path_exponent)
self.output_debug_file(
img, self.minimum_path_powered,
"minimal_path_power_" + str(self.minimum_path_exponent))
# Saving in Image since smooth_minimal_path needs pixel dimensions
img.data = self.minimum_path_powered
# smooth resulting minimal path
self.smoothed_min_path = smooth_minimal_path(img)
self.output_debug_file(img, self.smoothed_min_path.data,
"minimal_path_smooth")
# normalise symmetry values between 0 and 1
if self.enable_symmetry:
normalised_symmetry = normalize_array_histogram(self.raw_symmetry)
self.output_debug_file(img, self.smoothed_min_path.data,
"minimal_path_smooth")
# multiply normalised symmetry data with the minimum path result
from msct_image import change_data_orientation
self.spine_detect_data = np.multiply(
self.smoothed_min_path.data,
change_data_orientation(
np.power(normalised_symmetry, self.symmetry_exponent),
self.raw_orientation, "RPI"))
self.output_debug_file(img, self.spine_detect_data,
"symmetry_x_min_path")
# extract the centerline from the minimal path image
self.centerline_with_outliers = get_centerline(
self.spine_detect_data, self.spine_detect_data.shape)
else:
# extract the centerline from the minimal path image
self.centerline_with_outliers = get_centerline(
self.smoothed_min_path.data, self.smoothed_min_path.data.shape)
self.output_debug_file(img, self.centerline_with_outliers,
"centerline_with_outliers")
# saving centerline with outliers to have
img.data = self.centerline_with_outliers
img.change_orientation()
img.file_name = "centerline_with_outliers"
img.save()
# use a b-spline to smooth out the centerline
x, y, z, dx, dy, dz = smooth_centerline(
"centerline_with_outliers.nii.gz")
# save the centerline
nx, ny, nz, nt, px, py, pz, pt = img.dim
img.data = np.zeros((nx, ny, nz))
for i in range(0, np.size(x) - 1):
img.data[int(x[i]), int(y[i]), int(z[i])] = 1
self.output_debug_file(img, img.data, "centerline")
img.change_orientation(self.raw_orientation)
img.file_name = "centerline"
img.save()
# copy back centerline
os.chdir('../')
conv.convert(path_tmp + img.file_name + img.ext, self.output_filename)
if self.rm_tmp_file == 1:
import shutil
shutil.rmtree(path_tmp)
print "To view the output with FSL :"
sct.printv(
"fslview " + self.input_image.absolutepath + " " +
self.output_filename + " -l Red", self.verbose, "info")
def execute(self):
print 'Execution of the SCAD algorithm in '+str(os.getcwd())
original_name = self.input_image.file_name
vesselness_file_name = "imageVesselNessFilter.nii.gz"
raw_file_name = "raw.nii"
self.setup_debug_folder()
if self.debug:
import matplotlib.pyplot as plt # import for debug purposes
# create tmp and copy input
path_tmp = self.create_temporary_path()
conv.convert(self.input_image.absolutepath, path_tmp+raw_file_name)
if self.vesselness_provided:
sct.run('cp '+vesselness_file_name+' '+path_tmp+vesselness_file_name)
os.chdir(path_tmp)
# get input image information
img = Image(raw_file_name)
# save original orientation and change image to RPI
self.raw_orientation = img.change_orientation()
# get body symmetry
if self.enable_symmetry:
from msct_image import change_data_orientation
sym = SymmetryDetector(raw_file_name, self.contrast, crop_xy=0)
self.raw_symmetry = sym.execute()
img.change_orientation(self.raw_orientation)
self.output_debug_file(img, self.raw_symmetry, "body_symmetry")
img.change_orientation()
# vesselness filter
if not self.vesselness_provided:
sct.run('sct_vesselness -i '+raw_file_name+' -t ' + self._contrast+" -radius "+str(self.spinalcord_radius))
# load vesselness filter data and perform minimum path on it
img = Image(vesselness_file_name)
img.change_orientation()
self.minimum_path_data, self.J1_min_path, self.J2_min_path = get_minimum_path(img.data, invert=1, debug=1)
self.output_debug_file(img, self.minimum_path_data, "minimal_path")
self.output_debug_file(img, self.J1_min_path, "J1_minimal_path")
self.output_debug_file(img, self.J2_min_path, "J2_minimal_path")
# Apply an exponent to the minimum path
self.minimum_path_powered = np.power(self.minimum_path_data, self.minimum_path_exponent)
self.output_debug_file(img, self.minimum_path_powered, "minimal_path_power_"+str(self.minimum_path_exponent))
# Saving in Image since smooth_minimal_path needs pixel dimensions
img.data = self.minimum_path_powered
# smooth resulting minimal path
self.smoothed_min_path = smooth_minimal_path(img)
self.output_debug_file(img, self.smoothed_min_path.data, "minimal_path_smooth")
# normalise symmetry values between 0 and 1
if self.enable_symmetry:
normalised_symmetry = normalize_array_histogram(self.raw_symmetry)
self.output_debug_file(img, self.smoothed_min_path.data, "minimal_path_smooth")
# multiply normalised symmetry data with the minimum path result
from msct_image import change_data_orientation
self.spine_detect_data = np.multiply(self.smoothed_min_path.data, change_data_orientation(np.power(normalised_symmetry, self.symmetry_exponent), self.raw_orientation, "RPI"))
self.output_debug_file(img, self.spine_detect_data, "symmetry_x_min_path")
# extract the centerline from the minimal path image
self.centerline_with_outliers = get_centerline(self.spine_detect_data, self.spine_detect_data.shape)
else:
# extract the centerline from the minimal path image
self.centerline_with_outliers = get_centerline(self.smoothed_min_path.data, self.smoothed_min_path.data.shape)
self.output_debug_file(img, self.centerline_with_outliers, "centerline_with_outliers")
# saving centerline with outliers to have
img.data = self.centerline_with_outliers
img.change_orientation()
img.file_name = "centerline_with_outliers"
img.save()
# use a b-spline to smooth out the centerline
x, y, z, dx, dy, dz = smooth_centerline("centerline_with_outliers.nii.gz")
# save the centerline
nx, ny, nz, nt, px, py, pz, pt = img.dim
img.data = np.zeros((nx, ny, nz))
for i in range(0, np.size(x)-1):
img.data[int(x[i]), int(y[i]), int(z[i])] = 1
self.output_debug_file(img, img.data, "centerline")
img.change_orientation(self.raw_orientation)
img.file_name = "centerline"
img.save()
# copy back centerline
os.chdir('../')
conv.convert(path_tmp+img.file_name+img.ext, self.output_filename)
if self.rm_tmp_file == 1:
import shutil
shutil.rmtree(path_tmp)
def generate_initial_template_space(dataset_info, points_average_centerline, position_template_disks):
"""
This function generates the initial template space, on which all images will be registered.
:param points_average_centerline: list of points (x, y, z) of the average spinal cord and brainstem centerline
:param position_template_disks: index of intervertebral disks along the template centerline
:return:
"""
# initializing variables
path_template = dataset_info['path_template']
x_size_of_template_space, y_size_of_template_space = 201, 201
spacing = 0.5
# creating template space
size_template_z = int(abs(points_average_centerline[0][2] - points_average_centerline[-1][2]) / spacing) + 15
template_space = Image([x_size_of_template_space, y_size_of_template_space, size_template_z])
template_space.data = np.zeros((x_size_of_template_space, y_size_of_template_space, size_template_z))
template_space.hdr.set_data_dtype('float32')
origin = [points_average_centerline[-1][0] + x_size_of_template_space * spacing / 2.0,
points_average_centerline[-1][1] - y_size_of_template_space * spacing / 2.0,
(points_average_centerline[-1][2] - spacing)]
template_space.hdr.as_analyze_map()['dim'] = [3.0, x_size_of_template_space, y_size_of_template_space, size_template_z, 1.0, 1.0, 1.0, 1.0]
template_space.hdr.as_analyze_map()['qoffset_x'] = origin[0]
template_space.hdr.as_analyze_map()['qoffset_y'] = origin[1]
template_space.hdr.as_analyze_map()['qoffset_z'] = origin[2]
template_space.hdr.as_analyze_map()['srow_x'][-1] = origin[0]
template_space.hdr.as_analyze_map()['srow_y'][-1] = origin[1]
template_space.hdr.as_analyze_map()['srow_z'][-1] = origin[2]
template_space.hdr.as_analyze_map()['srow_x'][0] = -spacing
template_space.hdr.as_analyze_map()['srow_y'][1] = spacing
template_space.hdr.as_analyze_map()['srow_z'][2] = spacing
template_space.hdr.set_sform(template_space.hdr.get_sform())
template_space.hdr.set_qform(template_space.hdr.get_sform())
template_space.setFileName(path_template + 'template_space.nii.gz')
template_space.save(type='uint8')
# generate template centerline as an image
image_centerline = template_space.copy()
for coord in points_average_centerline:
coord_pix = image_centerline.transfo_phys2pix([coord])[0]
if 0 <= coord_pix[0] < image_centerline.data.shape[0] and 0 <= coord_pix[1] < image_centerline.data.shape[1] and 0 <= coord_pix[2] < image_centerline.data.shape[2]:
image_centerline.data[int(coord_pix[0]), int(coord_pix[1]), int(coord_pix[2])] = 1
image_centerline.setFileName(path_template + 'template_centerline.nii.gz')
image_centerline.save(type='float32')
# generate template disks position
coord_physical = []
image_disks = template_space.copy()
for disk in position_template_disks:
label = labels_regions[disk]
coord = position_template_disks[disk]
coord_pix = image_disks.transfo_phys2pix([coord])[0]
coord = coord.tolist()
coord.append(label)
coord_physical.append(coord)
if 0 <= coord_pix[0] < image_disks.data.shape[0] and 0 <= coord_pix[1] < image_disks.data.shape[1] and 0 <= coord_pix[2] < image_disks.data.shape[2]:
image_disks.data[int(coord_pix[0]), int(coord_pix[1]), int(coord_pix[2])] = label
else:
sct.printv(str(coord_pix))
sct.printv('ERROR: the disk label ' + str(disk) + ' is not in the template image.')
image_disks.setFileName(path_template + 'template_disks.nii.gz')
image_disks.save(type='uint8')
# generate template centerline as a npz file
x_centerline_fit, y_centerline_fit, z_centerline, x_centerline_deriv, y_centerline_deriv, z_centerline_deriv = smooth_centerline(
path_template + 'template_centerline.nii.gz', algo_fitting='nurbs', verbose=0, nurbs_pts_number=4000,
all_slices=False, phys_coordinates=True, remove_outliers=True)
centerline_template = Centerline(x_centerline_fit, y_centerline_fit, z_centerline,
x_centerline_deriv, y_centerline_deriv, z_centerline_deriv)
centerline_template.compute_vertebral_distribution(coord_physical)
centerline_template.save_centerline(fname_output=path_template + 'template_centerline')
def compute_dti(fname_in, fname_bvals, fname_bvecs, prefix, method, file_mask):
"""
Compute DTI.
:param fname_in: input 4d file.
:param bvals: bvals txt file
:param bvecs: bvecs txt file
:param prefix: output prefix. Example: "dti_"
:param method: algo for computing dti
:return: True/False
"""
# Open file.
from msct_image import Image
nii = Image(fname_in)
data = nii.data
print('data.shape (%d, %d, %d, %d)' % data.shape)
# open bvecs/bvals
from dipy.io import read_bvals_bvecs
bvals, bvecs = read_bvals_bvecs(fname_bvals, fname_bvecs)
from dipy.core.gradients import gradient_table
gtab = gradient_table(bvals, bvecs)
# mask and crop the data. This is a quick way to avoid calculating Tensors on the background of the image.
if not file_mask == '':
printv('Open mask file...', param.verbose)
# open mask file
nii_mask = Image(file_mask)
mask = nii_mask.data
# fit tensor model
printv('Computing tensor using "'+method+'" method...', param.verbose)
import dipy.reconst.dti as dti
if method == 'standard':
tenmodel = dti.TensorModel(gtab)
if file_mask == '':
tenfit = tenmodel.fit(data)
else:
tenfit = tenmodel.fit(data, mask)
elif method == 'restore':
import dipy.denoise.noise_estimate as ne
sigma = ne.estimate_sigma(data)
dti_restore = dti.TensorModel(gtab, fit_method='RESTORE', sigma=sigma)
if file_mask == '':
tenfit = dti_restore.fit(data)
else:
tenfit = dti_restore.fit(data, mask)
# Compute metrics
printv('Computing metrics...', param.verbose)
# FA
from dipy.reconst.dti import fractional_anisotropy
nii.data = fractional_anisotropy(tenfit.evals)
nii.setFileName(prefix+'FA.nii.gz')
nii.save('float32')
# MD
from dipy.reconst.dti import mean_diffusivity
nii.data = mean_diffusivity(tenfit.evals)
nii.setFileName(prefix+'MD.nii.gz')
nii.save('float32')
# RD
from dipy.reconst.dti import radial_diffusivity
nii.data = radial_diffusivity(tenfit.evals)
nii.setFileName(prefix+'RD.nii.gz')
nii.save('float32')
# AD
from dipy.reconst.dti import axial_diffusivity
nii.data = axial_diffusivity(tenfit.evals)
nii.setFileName(prefix+'AD.nii.gz')
nii.save('float32')
return True
def register(src, dest, paramreg, param, i_step_str):
# initiate default parameters of antsRegistration transformation
ants_registration_params = {'rigid': '', 'affine': '', 'compositeaffine': '', 'similarity': '', 'translation': '',
'bspline': ',10', 'gaussiandisplacementfield': ',3,0',
'bsplinedisplacementfield': ',5,10', 'syn': ',3,0', 'bsplinesyn': ',1,3'}
output = '' # default output if problem
# display arguments
sct.printv('Registration parameters:', param.verbose)
sct.printv(' type ........... '+paramreg.steps[i_step_str].type, param.verbose)
sct.printv(' algo ........... '+paramreg.steps[i_step_str].algo, param.verbose)
sct.printv(' slicewise ...... '+paramreg.steps[i_step_str].slicewise, param.verbose)
sct.printv(' metric ......... '+paramreg.steps[i_step_str].metric, param.verbose)
sct.printv(' iter ........... '+paramreg.steps[i_step_str].iter, param.verbose)
sct.printv(' smooth ......... '+paramreg.steps[i_step_str].smooth, param.verbose)
sct.printv(' laplacian ...... '+paramreg.steps[i_step_str].laplacian, param.verbose)
sct.printv(' shrink ......... '+paramreg.steps[i_step_str].shrink, param.verbose)
sct.printv(' gradStep ....... '+paramreg.steps[i_step_str].gradStep, param.verbose)
sct.printv(' init ........... '+paramreg.steps[i_step_str].init, param.verbose)
sct.printv(' poly ........... '+paramreg.steps[i_step_str].poly, param.verbose)
sct.printv(' dof ............ '+paramreg.steps[i_step_str].dof, param.verbose)
sct.printv(' smoothWarpXY ... '+paramreg.steps[i_step_str].smoothWarpXY, param.verbose)
# set metricSize
if paramreg.steps[i_step_str].metric == 'MI':
metricSize = '32' # corresponds to number of bins
else:
metricSize = '4' # corresponds to radius (for CC, MeanSquares...)
# set masking
if param.fname_mask:
fname_mask = 'mask.nii.gz'
masking = '-x mask.nii.gz'
else:
fname_mask = ''
masking = ''
if paramreg.steps[i_step_str].algo == 'slicereg':
# check if user used type=label
if paramreg.steps[i_step_str].type == 'label':
sct.printv('\nERROR: this algo is not compatible with type=label. Please use type=im or type=seg', 1, 'error')
else:
from msct_image import find_zmin_zmax
# threshold images (otherwise, automatic crop does not work -- see issue #293)
src_th = sct.add_suffix(src, '_th')
from msct_image import Image
nii = Image(src)
data = nii.data
data[data < 0.1] = 0
nii.data = data
nii.setFileName(src_th)
nii.save()
# sct.run(fsloutput+'fslmaths '+src+' -thr 0.1 '+src_th, param.verbose)
dest_th = sct.add_suffix(dest, '_th')
nii = Image(dest)
data = nii.data
data[data < 0.1] = 0
nii.data = data
nii.setFileName(dest_th)
nii.save()
# sct.run(fsloutput+'fslmaths '+dest+' -thr 0.1 '+dest_th, param.verbose)
# find zmin and zmax
zmin_src, zmax_src = find_zmin_zmax(src_th)
zmin_dest, zmax_dest = find_zmin_zmax(dest_th)
zmin_total = max([zmin_src, zmin_dest])
zmax_total = min([zmax_src, zmax_dest])
# crop data
src_crop = sct.add_suffix(src, '_crop')
sct.run('sct_crop_image -i '+src+' -o '+src_crop+' -dim 2 -start '+str(zmin_total)+' -end '+str(zmax_total), param.verbose)
dest_crop = sct.add_suffix(dest, '_crop')
sct.run('sct_crop_image -i '+dest+' -o '+dest_crop+' -dim 2 -start '+str(zmin_total)+' -end '+str(zmax_total), param.verbose)
# update variables
src = src_crop
dest = dest_crop
scr_regStep = sct.add_suffix(src, '_regStep'+i_step_str)
# estimate transfo
cmd = ('isct_antsSliceRegularizedRegistration '
'-t Translation[0.5] '
'-m '+paramreg.steps[i_step_str].metric+'['+dest+','+src+',1,'+metricSize+',Regular,0.2] '
'-p '+paramreg.steps[i_step_str].poly+' '
'-i '+paramreg.steps[i_step_str].iter+' '
'-f 1 '
'-s '+paramreg.steps[i_step_str].smooth+' '
'-v 1 ' # verbose (verbose=2 does not exist, so we force it to 1)
'-o [step'+i_step_str+','+scr_regStep+'] ' # here the warp name is stage10 because antsSliceReg add "Warp"
+masking)
warp_forward_out = 'step'+i_step_str+'Warp.nii.gz'
warp_inverse_out = 'step'+i_step_str+'InverseWarp.nii.gz'
# run command
status, output = sct.run(cmd, param.verbose)
# ANTS 3d
elif paramreg.steps[i_step_str].algo.lower() in ants_registration_params and paramreg.steps[i_step_str].slicewise == '0':
# make sure type!=label. If type==label, this will be addressed later in the code.
if not paramreg.steps[i_step_str].type == 'label':
# Pad the destination image (because ants doesn't deform the extremities)
# N.B. no need to pad if iter = 0
if not paramreg.steps[i_step_str].iter == '0':
dest_pad = sct.add_suffix(dest, '_pad')
sct.run('sct_image -i '+dest+' -o '+dest_pad+' -pad 0,0,'+str(param.padding))
dest = dest_pad
# apply Laplacian filter
if not paramreg.steps[i_step_str].laplacian == '0':
sct.printv('\nApply Laplacian filter', param.verbose)
sct.run('sct_maths -i '+src+' -laplacian '+paramreg.steps[i_step_str].laplacian+','+paramreg.steps[i_step_str].laplacian+',0 -o '+sct.add_suffix(src, '_laplacian'))
sct.run('sct_maths -i '+dest+' -laplacian '+paramreg.steps[i_step_str].laplacian+','+paramreg.steps[i_step_str].laplacian+',0 -o '+sct.add_suffix(dest, '_laplacian'))
src = sct.add_suffix(src, '_laplacian')
dest = sct.add_suffix(dest, '_laplacian')
# Estimate transformation
sct.printv('\nEstimate transformation', param.verbose)
scr_regStep = sct.add_suffix(src, '_regStep' + i_step_str)
cmd = ('isct_antsRegistration '
'--dimensionality 3 '
'--transform '+paramreg.steps[i_step_str].algo+'['+paramreg.steps[i_step_str].gradStep +
ants_registration_params[paramreg.steps[i_step_str].algo.lower()]+'] '
'--metric '+paramreg.steps[i_step_str].metric+'['+dest+','+src+',1,'+metricSize+'] '
'--convergence '+paramreg.steps[i_step_str].iter+' '
'--shrink-factors '+paramreg.steps[i_step_str].shrink+' '
'--smoothing-sigmas '+paramreg.steps[i_step_str].smooth+'mm '
'--restrict-deformation 1x1x0 '
'--output [step'+i_step_str+','+scr_regStep+'] '
'--interpolation BSpline[3] '
+masking)
# add verbose
if param.verbose >= 1:
cmd += ' --verbose 1'
# add init translation
if not paramreg.steps[i_step_str].init == '':
init_dict = {'geometric': '0', 'centermass': '1', 'origin': '2'}
cmd += ' -r ['+dest+','+src+','+init_dict[paramreg.steps[i_step_str].init]+']'
# run command
status, output = sct.run(cmd, param.verbose)
# get appropriate file name for transformation
if paramreg.steps[i_step_str].algo in ['rigid', 'affine', 'translation']:
warp_forward_out = 'step'+i_step_str+'0GenericAffine.mat'
warp_inverse_out = '-step'+i_step_str+'0GenericAffine.mat'
else:
warp_forward_out = 'step'+i_step_str+'0Warp.nii.gz'
warp_inverse_out = 'step'+i_step_str+'0InverseWarp.nii.gz'
# ANTS 2d
elif paramreg.steps[i_step_str].algo.lower() in ants_registration_params and paramreg.steps[i_step_str].slicewise == '1':
# make sure type!=label. If type==label, this will be addressed later in the code.
if not paramreg.steps[i_step_str].type == 'label':
from msct_register import register_slicewise
# if shrink!=1, force it to be 1 (otherwise, it generates a wrong 3d warping field). TODO: fix that!
if not paramreg.steps[i_step_str].shrink == '1':
sct.printv('\nWARNING: when using slicewise with SyN or BSplineSyN, shrink factor needs to be one. Forcing shrink=1.', 1, 'warning')
paramreg.steps[i_step_str].shrink = '1'
warp_forward_out = 'step'+i_step_str + 'Warp.nii.gz'
warp_inverse_out = 'step'+i_step_str + 'InverseWarp.nii.gz'
register_slicewise(src,
dest,
paramreg=paramreg.steps[i_step_str],
fname_mask=fname_mask,
warp_forward_out=warp_forward_out,
warp_inverse_out=warp_inverse_out,
ants_registration_params=ants_registration_params,
path_qc=param.path_qc,
verbose=param.verbose)
# slice-wise transfo
elif paramreg.steps[i_step_str].algo in ['centermass', 'centermassrot', 'columnwise']:
# if type=im, sends warning
if paramreg.steps[i_step_str].type == 'im':
sct.printv('\nWARNING: algo '+paramreg.steps[i_step_str].algo+' should be used with type=seg.\n', 1, 'warning')
# if type=label, exit with error
elif paramreg.steps[i_step_str].type == 'label':
sct.printv('\nERROR: this algo is not compatible with type=label. Please use type=im or type=seg', 1, 'error')
# check if user provided a mask-- if so, inform it will be ignored
if not fname_mask == '':
sct.printv('\nWARNING: algo '+paramreg.steps[i_step_str].algo+' will ignore the provided mask.\n', 1, 'warning')
# smooth data
if not paramreg.steps[i_step_str].smooth == '0':
sct.printv('\nSmooth data', param.verbose)
sct.run('sct_maths -i '+src+' -smooth '+paramreg.steps[i_step_str].smooth+','+paramreg.steps[i_step_str].smooth+',0 -o '+sct.add_suffix(src, '_smooth'))
sct.run('sct_maths -i '+dest+' -smooth '+paramreg.steps[i_step_str].smooth+','+paramreg.steps[i_step_str].smooth+',0 -o '+sct.add_suffix(dest, '_smooth'))
src = sct.add_suffix(src, '_smooth')
dest = sct.add_suffix(dest, '_smooth')
from msct_register import register_slicewise
warp_forward_out = 'step'+i_step_str + 'Warp.nii.gz'
warp_inverse_out = 'step'+i_step_str + 'InverseWarp.nii.gz'
register_slicewise(src,
dest,
paramreg=paramreg.steps[i_step_str],
fname_mask=fname_mask,
warp_forward_out=warp_forward_out,
warp_inverse_out=warp_inverse_out,
ants_registration_params=ants_registration_params,
path_qc=param.path_qc,
verbose=param.verbose)
else:
sct.printv('\nERROR: algo '+paramreg.steps[i_step_str].algo+' does not exist. Exit program\n', 1, 'error')
# landmark-based registration
if paramreg.steps[i_step_str].type in ['label']:
# check if user specified ilabel and dlabel
# TODO
warp_forward_out = 'step' + i_step_str + '0GenericAffine.txt'
warp_inverse_out = '-step' + i_step_str + '0GenericAffine.txt'
from msct_register_landmarks import register_landmarks
register_landmarks(src,
dest,
paramreg.steps[i_step_str].dof,
fname_affine=warp_forward_out,
verbose=param.verbose,
path_qc=param.path_qc)
if not os.path.isfile(warp_forward_out):
# no forward warping field for rigid and affine
sct.printv('\nERROR: file '+warp_forward_out+' doesn\'t exist (or is not a file).\n' + output +
'\nERROR: ANTs failed. Exit program.\n', 1, 'error')
elif not os.path.isfile(warp_inverse_out) and paramreg.steps[i_step_str].algo not in ['rigid', 'affine', 'translation'] and paramreg.steps[i_step_str].type not in ['label']:
# no inverse warping field for rigid and affine
sct.printv('\nERROR: file '+warp_inverse_out+' doesn\'t exist (or is not a file).\n' + output +
'\nERROR: ANTs failed. Exit program.\n', 1, 'error')
else:
# rename warping fields
if (paramreg.steps[i_step_str].algo.lower() in ['rigid', 'affine', 'translation'] and paramreg.steps[i_step_str].slicewise == '0'):
# if ANTs is used with affine/rigid --> outputs .mat file
warp_forward = 'warp_forward_'+i_step_str+'.mat'
os.rename(warp_forward_out, warp_forward)
warp_inverse = '-warp_forward_'+i_step_str+'.mat'
elif paramreg.steps[i_step_str].type in ['label']:
# if label-based registration is used --> outputs .txt file
warp_forward = 'warp_forward_'+i_step_str+'.txt'
os.rename(warp_forward_out, warp_forward)
warp_inverse = '-warp_forward_'+i_step_str+'.txt'
else:
warp_forward = 'warp_forward_'+i_step_str+'.nii.gz'
warp_inverse = 'warp_inverse_'+i_step_str+'.nii.gz'
os.rename(warp_forward_out, warp_forward)
os.rename(warp_inverse_out, warp_inverse)
return warp_forward, warp_inverse
def main(args=None):
# Initialization
# fname_anat = ''
# fname_centerline = ''
sigma = 3 # default value of the standard deviation for the Gaussian smoothing (in terms of number of voxels)
# remove_temp_files = param.remove_temp_files
# verbose = param.verbose
start_time = time.time()
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
fname_anat = arguments['-i']
fname_centerline = arguments['-s']
if '-smooth' in arguments:
sigma = arguments['-smooth']
if '-r' in arguments:
remove_temp_files = int(arguments['-r'])
if '-v' in arguments:
verbose = int(arguments['-v'])
# Display arguments
sct.printv('\nCheck input arguments...')
sct.printv(' Volume to smooth .................. ' + fname_anat)
sct.printv(' Centerline ........................ ' + fname_centerline)
sct.printv(' Sigma (mm) ........................ ' + str(sigma))
sct.printv(' Verbose ........................... ' + str(verbose))
# Check that input is 3D:
from msct_image import Image
nx, ny, nz, nt, px, py, pz, pt = Image(fname_anat).dim
dim = 4 # by default, will be adjusted later
if nt == 1:
dim = 3
if nz == 1:
dim = 2
if dim == 4:
sct.printv('WARNING: the input image is 4D, please split your image to 3D before smoothing spinalcord using :\n'
'sct_image -i ' + fname_anat + ' -split t -o ' + fname_anat, verbose, 'warning')
sct.printv('4D images not supported, aborting ...', verbose, 'error')
# Extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
path_centerline, file_centerline, ext_centerline = sct.extract_fname(fname_centerline)
path_tmp = sct.tmp_create(basename="smooth_spinalcord", verbose=verbose)
# Copying input data to tmp folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
sct.copy(fname_anat, os.path.join(path_tmp, "anat" + ext_anat))
sct.copy(fname_centerline, os.path.join(path_tmp, "centerline" + ext_centerline))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# convert to nii format
convert('anat' + ext_anat, 'anat.nii')
convert('centerline' + ext_centerline, 'centerline.nii')
# Change orientation of the input image into RPI
sct.printv('\nOrient input volume to RPI orientation...')
fname_anat_rpi = set_orientation('anat.nii', 'RPI', filename=True)
shutil.move(fname_anat_rpi, 'anat_rpi.nii')
# Change orientation of the input image into RPI
sct.printv('\nOrient centerline to RPI orientation...')
fname_centerline_rpi = set_orientation('centerline.nii', 'RPI', filename=True)
shutil.move(fname_centerline_rpi, 'centerline_rpi.nii')
# Straighten the spinal cord
# straighten segmentation
sct.printv('\nStraighten the spinal cord using centerline/segmentation...', verbose)
cache_sig = sct.cache_signature(
input_files=["anat_rpi.nii", "centerline_rpi.nii"],
input_params={"x": "spline"},
)
cachefile = os.path.join(curdir, "straightening.cache")
if sct.cache_valid(cachefile, cache_sig) and os.path.isfile(os.path.join(curdir, 'warp_curve2straight.nii.gz')) and os.path.isfile(os.path.join(curdir, 'warp_straight2curve.nii.gz')) and os.path.isfile(os.path.join(curdir, 'straight_ref.nii.gz')):
# if they exist, copy them into current folder
sct.printv('Reusing existing warping field which seems to be valid', verbose, 'warning')
sct.copy(os.path.join(curdir, 'warp_curve2straight.nii.gz'), 'warp_curve2straight.nii.gz')
sct.copy(os.path.join(curdir, 'warp_straight2curve.nii.gz'), 'warp_straight2curve.nii.gz')
sct.copy(os.path.join(curdir, 'straight_ref.nii.gz'), 'straight_ref.nii.gz')
# apply straightening
sct.run(['sct_apply_transfo', '-i', 'anat_rpi.nii', '-w', 'warp_curve2straight.nii.gz', '-d', 'straight_ref.nii.gz', '-o', 'anat_rpi_straight.nii', '-x', 'spline'], verbose)
else:
sct.run(['sct_straighten_spinalcord', '-i', 'anat_rpi.nii', '-s', 'centerline_rpi.nii', '-x', 'spline'], verbose)
sct.cache_save(cachefile, cache_sig)
# Smooth the straightened image along z
sct.printv('\nSmooth the straightened image along z...')
sct.run(['sct_maths', '-i', 'anat_rpi_straight.nii', '-smooth', '0,0,' + str(sigma), '-o', 'anat_rpi_straight_smooth.nii'], verbose)
# Apply the reversed warping field to get back the curved spinal cord
sct.printv('\nApply the reversed warping field to get back the curved spinal cord...')
sct.run(['sct_apply_transfo', '-i', 'anat_rpi_straight_smooth.nii', '-o', 'anat_rpi_straight_smooth_curved.nii', '-d', 'anat.nii', '-w', 'warp_straight2curve.nii.gz', '-x', 'spline'], verbose)
# replace zeroed voxels by original image (issue #937)
sct.printv('\nReplace zeroed voxels by original image...', verbose)
nii_smooth = Image('anat_rpi_straight_smooth_curved.nii')
data_smooth = nii_smooth.data
data_input = Image('anat.nii').data
indzero = np.where(data_smooth == 0)
data_smooth[indzero] = data_input[indzero]
nii_smooth.data = data_smooth
nii_smooth.setFileName('anat_rpi_straight_smooth_curved_nonzero.nii')
nii_smooth.save()
# come back
os.chdir(curdir)
# Generate output file
sct.printv('\nGenerate output file...')
sct.generate_output_file(os.path.join(path_tmp, "anat_rpi_straight_smooth_curved_nonzero.nii"),
file_anat + '_smooth' + ext_anat)
# Remove temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...')
sct.rmtree(path_tmp)
# Display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(round(elapsed_time))) + 's\n')
sct.display_viewer_syntax([file_anat, file_anat + '_smooth'], verbose=verbose)
import os
import commands
import sys
from shutil import move
# Get path of the toolbox
status, path_sct = commands.getstatusoutput('echo $SCT_DIR')
# Append path that contains scripts, to be able to load modules
sys.path.append(path_sct + '/scripts')
from msct_image import Image
import sct_utils as sct
path_template = '/Users/julien/data/PAM50/template'
folder_PAM50 = 'PAM50/template/'
os.chdir(path_template)
sct.create_folder(folder_PAM50)
for file_template in [
'MNI-Poly-AMU_T1.nii.gz', 'MNI-Poly-AMU_T2.nii.gz',
'MNI-Poly-AMU_T2star.nii.gz'
]:
im = Image(file_template)
# remove negative values
data = im.data
data[data < 0] = 0
im.data = data
im.changeType('uint16')
file_new = file_template.replace('MNI-Poly-AMU', 'PAM50')
im.setFileName(file_new)
im.save()
# move to folder
move(file_new, folder_PAM50 + file_new)
def execute(self):
print 'Execution of the SCAD algorithm'
vesselness_file_name = "imageVesselNessFilter.nii.gz"
raw_file_name = "raw.nii"
if self.debug:
import matplotlib.pyplot as plt # import for debug purposes
# create tmp and copy input
path_tmp = sct.tmp_create()
sct.tmp_copy_nifti(self.input_image.absolutepath, path_tmp, raw_file_name)
if self.vesselness_provided:
sct.run('cp '+vesselness_file_name+' '+path_tmp+vesselness_file_name)
os.chdir(path_tmp)
# get input image information
img = Image(raw_file_name)
# save original orientation and change image to RPI
self.raw_orientation = img.change_orientation()
# get body symmetry
sym = SymmetryDetector(raw_file_name, self.contrast, crop_xy=1)
self.raw_symmetry = sym.execute()
# vesselness filter
if not self.vesselness_provided:
sct.run('sct_vesselness -i '+raw_file_name+' -t ' + self._contrast)
# load vesselness filter data and perform minimum path on it
img = Image(vesselness_file_name)
raw_orientation = img.change_orientation()
self.minimum_path_data, self.J1_min_path, self.J2_min_path = get_minimum_path(img.data, invert=1, debug=1, smooth_factor=1)
# Apply an exponent to the minimum path
self.minimum_path_powered = np.power(self.minimum_path_data, self.minimum_path_exponent)
# Saving in Image since smooth_minimal_path needs pixel dimensions
img.data = self.minimum_path_powered
# smooth resulting minimal path
self.smoothed_min_path = smooth_minimal_path(img)
# normalise symmetry values between 0 and 1
normalised_symmetry = equalize_array_histogram(self.raw_symmetry)
# multiply normalised symmetry data with the minimum path result
self.spine_detect_data = np.multiply(self.smoothed_min_path.data, normalised_symmetry)
# extract the centerline from the minimal path image
centerline_with_outliers = get_centerline(self.spine_detect_data, self.spine_detect_data.shape)
img.data = centerline_with_outliers
img.change_orientation()
img.file_name = "centerline_with_outliers"
img.save()
# use a b-spline to smooth out the centerline
x, y, z, dx, dy, dz = smooth_centerline("centerline_with_outliers.nii.gz")
# save the centerline
centerline_dim = img.dim
img.data = np.zeros(centerline_dim)
for i in range(0, np.size(x)-1):
img.data[int(x[i]), int(y[i]), int(z[i])] = 1
img.change_orientation(raw_orientation)
img.file_name = "centerline"
img.save()
# copy back centerline
os.chdir('../')
sct.tmp_copy_nifti(path_tmp + 'centerline.nii.gz',self.input_image.path,self.input_image.file_name+'_centerline'+self.input_image.ext)
if self.rm_tmp_file == 1:
import shutil
shutil.rmtree(path_tmp)
if self.produce_output:
self.produce_output_files()
def dmri_moco(param):
file_data = 'dmri'
ext_data = '.nii'
file_b0 = 'b0'
file_dwi = 'dwi'
mat_final = 'mat_final/'
file_dwi_group = 'dwi_averaged_groups' # no extension
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
ext_mat = 'Warp.nii.gz' # warping field
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(file_data+'.nii').dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), param.verbose)
# Identify b=0 and DWI images
sct.printv('\nIdentify b=0 and DWI images...', param.verbose)
index_b0, index_dwi, nb_b0, nb_dwi = identify_b0('bvecs.txt', param.fname_bvals, param.bval_min, param.verbose)
# check if dmri and bvecs are the same size
if not nb_b0 + nb_dwi == nt:
sct.printv('\nERROR in '+os.path.basename(__file__)+': Size of data ('+str(nt)+') and size of bvecs ('+str(nb_b0+nb_dwi)+') are not the same. Check your bvecs file.\n', 1, 'error')
sys.exit(2)
# Prepare NIFTI (mean/groups...)
#===================================================================================================================
# Split into T dimension
sct.printv('\nSplit along T dimension...', param.verbose)
status, output = sct.run('sct_split_data -i ' + file_data + ext_data + ' -dim t -suffix _T', param.verbose)
# Merge b=0 images
sct.printv('\nMerge b=0...', param.verbose)
# cmd = fsloutput + 'fslmerge -t ' + file_b0
# for it in range(nb_b0):
# cmd = cmd + ' ' + file_data + '_T' + str(index_b0[it]).zfill(4)
cmd = 'sct_concat_data -dim t -o ' + file_b0 + ext_data + ' -i '
for it in range(nb_b0):
cmd = cmd + file_data + '_T' + str(index_b0[it]).zfill(4) + ext_data + ','
cmd = cmd[:-1] # remove ',' at the end of the string
status, output = sct.run(cmd, param.verbose)
sct.printv((' File created: ' + file_b0), param.verbose)
# Average b=0 images
sct.printv('\nAverage b=0...', param.verbose)
file_b0_mean = file_b0+'_mean'
sct.run('sct_maths -i '+file_b0+'.nii'+' -o '+file_b0_mean+'.nii'+' -mean t', param.verbose)
# if not average_data_across_dimension(file_b0+'.nii', file_b0_mean+'.nii', 3):
# sct.printv('ERROR in average_data_across_dimension', 1, 'error')
# cmd = fsloutput + 'fslmaths ' + file_b0 + ' -Tmean ' + file_b0_mean
# status, output = sct.run(cmd, param.verbose)
# Number of DWI groups
nb_groups = int(math.floor(nb_dwi/param.group_size))
# Generate groups indexes
group_indexes = []
for iGroup in range(nb_groups):
group_indexes.append(index_dwi[(iGroup*param.group_size):((iGroup+1)*param.group_size)])
# add the remaining images to the last DWI group
nb_remaining = nb_dwi%param.group_size # number of remaining images
if nb_remaining > 0:
nb_groups += 1
group_indexes.append(index_dwi[len(index_dwi)-nb_remaining:len(index_dwi)])
# DWI groups
for iGroup in range(nb_groups):
sct.printv('\nDWI group: ' +str((iGroup+1))+'/'+str(nb_groups), param.verbose)
# get index
index_dwi_i = group_indexes[iGroup]
nb_dwi_i = len(index_dwi_i)
# Merge DW Images
sct.printv('Merge DW images...', param.verbose)
file_dwi_merge_i = file_dwi + '_' + str(iGroup)
cmd = 'sct_concat_data -dim t -o ' + file_dwi_merge_i + ext_data + ' -i '
for it in range(nb_dwi_i):
cmd = cmd + file_data + '_T' + str(index_dwi_i[it]).zfill(4) + ext_data + ','
cmd = cmd[:-1] # remove ',' at the end of the string
sct.run(cmd, param.verbose)
# cmd = fsloutput + 'fslmerge -t ' + file_dwi_merge_i
# for it in range(nb_dwi_i):
# cmd = cmd +' ' + file_data + '_T' + str(index_dwi_i[it]).zfill(4)
# Average DW Images
sct.printv('Average DW images...', param.verbose)
file_dwi_mean = file_dwi + '_mean_' + str(iGroup)
sct.run('sct_maths -i '+file_dwi_merge_i+'.nii'+' -o '+file_dwi_mean+'.nii'+' -mean t', param.verbose)
# if not average_data_across_dimension(file_dwi_merge_i+'.nii', file_dwi_mean+'.nii', 3):
# sct.printv('ERROR in average_data_across_dimension', 1, 'error')
# cmd = fsloutput + 'fslmaths ' + file_dwi_merge_i + ' -Tmean ' + file_dwi_mean
# sct.run(cmd, param.verbose)
# Merge DWI groups means
sct.printv('\nMerging DW files...', param.verbose)
# file_dwi_groups_means_merge = 'dwi_averaged_groups'
cmd = 'sct_concat_data -dim t -o ' + file_dwi_group + ext_data + ' -i '
for iGroup in range(nb_groups):
cmd = cmd + file_dwi + '_mean_' + str(iGroup) + ext_data + ','
cmd = cmd[:-1] # remove ',' at the end of the string
sct.run(cmd, param.verbose)
# cmd = fsloutput + 'fslmerge -t ' + file_dwi_group
# for iGroup in range(nb_groups):
# cmd = cmd + ' ' + file_dwi + '_mean_' + str(iGroup)
# Average DW Images
# TODO: USEFULL ???
sct.printv('\nAveraging all DW images...', param.verbose)
fname_dwi_mean = 'dwi_mean'
sct.run('sct_maths -i '+file_dwi_group+'.nii'+' -o '+file_dwi_group+'_mean.nii'+' -mean t', param.verbose)
# if not average_data_across_dimension(file_dwi_group+'.nii', file_dwi_group+'_mean.nii', 3):
# sct.printv('ERROR in average_data_across_dimension', 1, 'error')
# sct.run(fsloutput + 'fslmaths ' + file_dwi_group + ' -Tmean ' + file_dwi_group+'_mean', param.verbose)
# segment dwi images using otsu algorithm
if param.otsu:
sct.printv('\nSegment group DWI using OTSU algorithm...', param.verbose)
# import module
otsu = importlib.import_module('sct_otsu')
# get class from module
param_otsu = otsu.param() #getattr(otsu, param)
param_otsu.fname_data = file_dwi_group+'.nii'
param_otsu.threshold = param.otsu
param_otsu.file_suffix = '_seg'
# run otsu
otsu.otsu(param_otsu)
file_dwi_group = file_dwi_group+'_seg'
# extract first DWI volume as target for registration
nii = Image(file_dwi_group+'.nii')
data_crop = nii.data[:, :, :, index_dwi[0]:index_dwi[0]+1]
nii.data = data_crop
nii.setFileName('target_dwi.nii')
nii.save()
# START MOCO
#===================================================================================================================
# Estimate moco on b0 groups
sct.printv('\n-------------------------------------------------------------------------------', param.verbose)
sct.printv(' Estimating motion on b=0 images...', param.verbose)
sct.printv('-------------------------------------------------------------------------------', param.verbose)
param_moco = param
param_moco.file_data = 'b0'
if index_dwi[0] != 0:
# If first DWI is not the first volume (most common), then there is a least one b=0 image before. In that case
# select it as the target image for registration of all b=0
param_moco.file_target = file_data + '_T' + str(index_b0[index_dwi[0]-1]).zfill(4)
else:
# If first DWI is the first volume, then the target b=0 is the first b=0 from the index_b0.
param_moco.file_target = file_data + '_T' + str(index_b0[0]).zfill(4)
param_moco.path_out = ''
param_moco.todo = 'estimate'
param_moco.mat_moco = 'mat_b0groups'
moco.moco(param_moco)
# Estimate moco on dwi groups
sct.printv('\n-------------------------------------------------------------------------------', param.verbose)
sct.printv(' Estimating motion on DW images...', param.verbose)
sct.printv('-------------------------------------------------------------------------------', param.verbose)
param_moco.file_data = file_dwi_group
param_moco.file_target = 'target_dwi' # target is the first DW image (closest to the first b=0)
param_moco.path_out = ''
#param_moco.todo = 'estimate'
param_moco.todo = 'estimate_and_apply'
param_moco.mat_moco = 'mat_dwigroups'
moco.moco(param_moco)
# create final mat folder
sct.create_folder(mat_final)
# Copy b=0 registration matrices
sct.printv('\nCopy b=0 registration matrices...', param.verbose)
for it in range(nb_b0):
sct.run('cp '+'mat_b0groups/'+'mat.T'+str(it)+ext_mat+' '+mat_final+'mat.T'+str(index_b0[it])+ext_mat, param.verbose)
# Copy DWI registration matrices
sct.printv('\nCopy DWI registration matrices...', param.verbose)
for iGroup in range(nb_groups):
for dwi in range(len(group_indexes[iGroup])):
sct.run('cp '+'mat_dwigroups/'+'mat.T'+str(iGroup)+ext_mat+' '+mat_final+'mat.T'+str(group_indexes[iGroup][dwi])+ext_mat, param.verbose)
# Spline Regularization along T
if param.spline_fitting:
moco.spline(mat_final, nt, nz, param.verbose, np.array(index_b0), param.plot_graph)
# combine Eddy Matrices
if param.run_eddy:
param.mat_2_combine = 'mat_eddy'
param.mat_final = mat_final
moco.combine_matrix(param)
# Apply moco on all dmri data
sct.printv('\n-------------------------------------------------------------------------------', param.verbose)
sct.printv(' Apply moco', param.verbose)
sct.printv('-------------------------------------------------------------------------------', param.verbose)
param_moco.file_data = 'dmri'
param_moco.file_target = file_dwi+'_mean_'+str(0) # reference for reslicing into proper coordinate system
param_moco.path_out = ''
param_moco.mat_moco = mat_final
param_moco.todo = 'apply'
moco.moco(param_moco)
# copy geometric information from header
# NB: this is required because WarpImageMultiTransform in 2D mode wrongly sets pixdim(3) to "1".
copy_header('dmri.nii', 'dmri_moco.nii')
# generate b0_moco_mean and dwi_moco_mean
cmd = 'sct_dmri_separate_b0_and_dwi -i dmri'+param.suffix+'.nii -b bvecs.txt -a 1'
if not param.fname_bvals == '':
cmd = cmd+' -m '+param.fname_bvals
sct.run(cmd, param.verbose)
def create_mask():
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
# display usage if a mandatory argument is not provided
if param.fname_data == '' or param.method == '':
sct.printv('\nERROR: All mandatory arguments are not provided. See usage (add -h).\n', 1, 'error')
# parse argument for method
method_list = param.method.replace(' ', '').split(',') # remove spaces and parse with comma
# method_list = param.method.split(',') # parse with comma
method_type = method_list[0]
# check existence of method type
if not method_type in param.method_list:
sct.printv('\nERROR in '+os.path.basename(__file__)+': Method "'+method_type+'" is not recognized. See usage (add -h).\n', 1, 'error')
# check method val
if not method_type == 'center':
method_val = method_list[1]
del method_list
# check existence of shape
if not param.shape in param.shape_list:
sct.printv('\nERROR in '+os.path.basename(__file__)+': Shape "'+param.shape+'" is not recognized. See usage (add -h).\n', 1, 'error')
# check existence of input files
sct.printv('\ncheck existence of input files...', param.verbose)
sct.check_file_exist(param.fname_data, param.verbose)
if method_type == 'centerline':
sct.check_file_exist(method_val, param.verbose)
# check if orientation is RPI
sct.printv('\nCheck if orientation is RPI...', param.verbose)
status, output = sct.run('sct_orientation -i '+param.fname_data)
if not output == 'RPI':
sct.printv('\nERROR in '+os.path.basename(__file__)+': Orientation of input image should be RPI. Use sct_orientation to put your image in RPI.\n', 1, 'error')
# display input parameters
sct.printv('\nInput parameters:', param.verbose)
sct.printv(' data ..................'+param.fname_data, param.verbose)
sct.printv(' method ................'+method_type, 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 = param.file_prefix+file_data+ext_data
#fname_out = os.path.abspath(path_out+file_out+ext_out)
# create temporary folder
sct.printv('\nCreate temporary folder...', param.verbose)
path_tmp = sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
sct.run('mkdir '+path_tmp, param.verbose)
# Copying input data to tmp folder and convert to nii
# NB: cannot use c3d here because c3d cannot convert 4D data.
sct.printv('\nCopying input data to tmp folder and convert to nii...', param.verbose)
convert(param.fname_data, path_tmp+'data.nii')
# sct.run('cp '+param.fname_data+' '+path_tmp+'data'+ext_data, param.verbose)
if method_type == 'centerline':
convert(method_val, path_tmp+'centerline.nii.gz')
# sct.run('isct_c3d '+method_val+' -o '+path_tmp+'/centerline.nii.gz')
# go to tmp folder
os.chdir(path_tmp)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = Image('data.nii').dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz)+ ' x ' + str(nt), 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 3D.', param.verbose, 'warning')
# extract first volume to have 3d reference
nii = Image('data.nii')
data3d = nii.data[:,:,:,0]
nii.data = data3d
nii.save()
if method_type == 'coord':
# parse to get coordinate
coord = map(int, method_val.split('x'))
if method_type == 'point':
# get file name
fname_point = method_val
# extract coordinate of point
sct.printv('\nExtract coordinate of point...', param.verbose)
status, output = sct.run('sct_label_utils -i '+fname_point+' -t display-voxel', param.verbose)
# parse to get coordinate
coord = output[output.find('Position=')+10:-17].split(',')
if method_type == 'center':
# set coordinate at center of FOV
coord = round(float(nx)/2), round(float(ny)/2)
if method_type == 'centerline':
# get name of centerline from user argument
fname_centerline = 'centerline.nii.gz'
else:
# generate volume with line along Z at coordinates 'coord'
sct.printv('\nCreate line...', param.verbose)
fname_centerline = create_line('data.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
data_centerline = centerline.get_data() # get centerline
z_centerline = [iz for iz in range(0, nz, 1) if data_centerline[:, :, iz].any()]
nz = len(z_centerline)
# get center of mass of the centerline
cx = [0] * nz
cy = [0] * nz
for iz in range(0, nz, 1):
cx[iz], cy[iz] = ndimage.measurements.center_of_mass(numpy.array(data_centerline[:, :, z_centerline[iz]]))
# create 2d masks
file_mask = 'data_mask'
for iz in range(nz):
center = numpy.array([cx[iz], cy[iz]])
mask2d = create_mask2d(center, param.shape, param.size, nx, ny)
# Write NIFTI volumes
img = nibabel.Nifti1Image(mask2d, None, hdr)
nibabel.save(img, (file_mask+str(iz)+'.nii'))
# merge along Z
# cmd = 'fslmerge -z mask '
cmd = 'sct_concat_data -dim z -o mask.nii.gz -i '
for iz in range(nz):
cmd = cmd + file_mask+str(iz)+'.nii,'
# remove ',' at the end of the string
cmd = cmd[:-1]
status, output = sct.run(cmd, param.verbose)
# copy geometry
copy_header('data.nii', 'mask.nii.gz')
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', param.verbose)
sct.generate_output_file(path_tmp+'mask.nii.gz', param.fname_out)
# Remove temporary files
if param.remove_tmp_files == 1:
sct.printv('\nRemove temporary files...', param.verbose)
sct.run('rm -rf '+path_tmp, param.verbose)
# to view results
sct.printv('\nDone! To view results, type:', param.verbose)
sct.printv('fslview '+param.fname_data+' '+param.fname_out+' -l Red -t 0.5 &', param.verbose, 'info')
print
def create_mask():
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
# 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 = param.file_prefix + file_data + ext_data
# create temporary folder
sct.printv('\nCreate temporary folder...', param.verbose)
path_tmp = sct.tmp_create(param.verbose)
# )sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
# sct.run('mkdir '+path_tmp, param.verbose)
sct.printv('\nCheck orientation...', param.verbose)
orientation_input = get_orientation(Image(param.fname_data))
sct.printv('.. ' + orientation_input, param.verbose)
reorient_coordinates = False
# copy input data to tmp folder
convert(param.fname_data, path_tmp + 'data.nii')
if method_type == 'centerline':
convert(method_val, path_tmp + 'centerline.nii.gz')
if method_type == 'point':
convert(method_val, path_tmp + 'point.nii.gz')
# go to tmp folder
os.chdir(path_tmp)
# reorient to RPI
sct.printv('\nReorient to RPI...', param.verbose)
# if not orientation_input == 'RPI':
sct.run('sct_image -i data.nii -o data_RPI.nii -setorient RPI -v 0',
verbose=False)
if method_type == 'centerline':
sct.run(
'sct_image -i centerline.nii.gz -o centerline_RPI.nii.gz -setorient RPI -v 0',
verbose=False)
if method_type == 'point':
sct.run(
'sct_image -i point.nii.gz -o point_RPI.nii.gz -setorient RPI -v 0',
verbose=False)
#
# if method_type == 'centerline':
# orientation_centerline = get_orientation_3d(method_val, filename=True)
# if not orientation_centerline == 'RPI':
# sct.run('sct_image -i ' + method_val + ' -o ' + path_tmp + 'centerline.nii.gz' + ' -setorient RPI -v 0', verbose=False)
# else:
# convert(method_val, path_tmp+'centerline.nii.gz')
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = Image('data_RPI.nii').dim
sct.printv(
' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt),
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 3D.', param.verbose,
'warning')
# extract first volume to have 3d reference
nii = Image('data_RPI.nii')
data3d = nii.data[:, :, :, 0]
nii.data = data3d
nii.save()
if method_type == 'coord':
# parse to get coordinate
coord = map(int, method_val.split('x'))
if method_type == 'point':
# get file name
fname_point = method_val
# 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.gz -display', param.verbose)
# parse to get coordinate
coord = output[output.find('Position=') + 10:-17].split(',')
if method_type == 'center':
# set coordinate at center of FOV
coord = round(float(nx) / 2), round(float(ny) / 2)
if method_type == 'centerline':
# get name of centerline from user argument
fname_centerline = 'centerline_RPI.nii.gz'
else:
# generate volume with line along Z at coordinates 'coord'
sct.printv('\nCreate line...', param.verbose)
fname_centerline = create_line('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(
numpy.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 = numpy.array([cx[iz], cy[iz]])
mask2d = create_mask2d(center,
param.shape,
param.size,
nx,
ny,
even=param.even,
spacing=spacing)
# Write NIFTI volumes
img = nibabel.Nifti1Image(mask2d, None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
# merge along Z
# cmd = 'fslmerge -z mask '
# CHANGE THAT CAN IMPACT SPEED:
# related to issue #755, we cannot open more than 256 files at one time.
# to solve this issue, we do not open more than 100 files
'''
im_list = []
im_temp = []
for iz in range(nz_not_null):
if iz != 0 and iz % 100 == 0:
im_temp.append(concat_data(im_list, 2))
im_list = [Image(file_mask + str(iz) + '.nii')]
else:
im_list.append(Image(file_mask+str(iz)+'.nii'))
if im_temp:
im_temp.append(concat_data(im_list, 2))
im_out = concat_data(im_temp, 2, no_expand=True)
else:
im_out = concat_data(im_list, 2)
'''
fname_list = [file_mask + str(iz) + '.nii' for iz in range(nz)]
im_out = concat_data(fname_list, dim=2)
im_out.setFileName('mask_RPI.nii.gz')
im_out.save()
# reorient if necessary
# if not orientation_input == 'RPI':
sct.run(
'sct_image -i mask_RPI.nii.gz -o mask.nii.gz -setorient ' +
orientation_input, param.verbose)
# copy header input --> mask
im_dat = Image('data.nii')
im_mask = Image('mask.nii.gz')
im_mask = copy_header(im_dat, im_mask)
im_mask.save()
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', param.verbose)
sct.generate_output_file(path_tmp + 'mask.nii.gz', param.fname_out)
# Remove temporary files
if param.remove_tmp_files == 1:
sct.printv('\nRemove temporary files...', param.verbose)
sct.run('rm -rf ' + path_tmp, param.verbose, error_exit='warning')
# to view results
sct.printv('\nDone! To view results, type:', param.verbose)
sct.printv(
'fslview ' + param.fname_data + ' ' + param.fname_out +
' -l Red -t 0.5 &', param.verbose, 'info')
print
def main(args = None):
dim_list = ['x', 'y', 'z', 't']
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(args)
fname_in = arguments["-i"]
fname_out = arguments["-o"]
verbose = int(arguments['-v'])
# Open file(s)
im = Image(fname_in)
data = im.data # 3d or 4d numpy array
dim = im.dim
# run command
if '-otsu' in arguments:
param = arguments['-otsu']
data_out = otsu(data, param)
elif '-otsu_adap' in arguments:
param = arguments['-otsu_adap']
data_out = otsu_adap(data, param[0], param[1])
elif '-otsu_median' in arguments:
param = arguments['-otsu_median']
data_out = otsu_median(data, param[0], param[1])
elif '-thr' in arguments:
param = arguments['-thr']
data_out = threshold(data, param)
elif '-percent' in arguments:
param = arguments['-percent']
data_out = perc(data, param)
elif '-bin' in arguments:
bin_thr = arguments['-bin']
data_out = binarise(data, bin_thr=bin_thr)
elif '-add' in arguments:
from numpy import sum
data2 = get_data_or_scalar(arguments["-add"], data)
data_concat = concatenate_along_4th_dimension(data, data2)
data_out = sum(data_concat, axis=3)
elif '-sub' in arguments:
data2 = get_data_or_scalar(arguments['-sub'], data)
data_out = data - data2
elif "-laplacian" in arguments:
sigmas = arguments["-laplacian"]
if len(sigmas) == 1:
sigmas = [sigmas for i in range(len(data.shape))]
elif len(sigmas) != len(data.shape):
printv(parser.usage.generate(error='ERROR: -laplacian need the same number of inputs as the number of image dimension OR only one input'))
# adjust sigma based on voxel size
sigmas = [sigmas[i] / dim[i+4] for i in range(3)]
# smooth data
data_out = laplacian(data, sigmas)
elif '-mul' in arguments:
from numpy import prod
data2 = get_data_or_scalar(arguments["-mul"], data)
data_concat = concatenate_along_4th_dimension(data, data2)
data_out = prod(data_concat, axis=3)
elif '-div' in arguments:
from numpy import divide
data2 = get_data_or_scalar(arguments["-div"], data)
data_out = divide(data, data2)
elif '-mean' in arguments:
from numpy import mean
dim = dim_list.index(arguments['-mean'])
if dim+1 > len(np.shape(data)): # in case input volume is 3d and dim=t
data = data[..., np.newaxis]
data_out = mean(data, dim)
elif '-std' in arguments:
from numpy import std
dim = dim_list.index(arguments['-std'])
if dim+1 > len(np.shape(data)): # in case input volume is 3d and dim=t
data = data[..., np.newaxis]
data_out = std(data, dim)
elif "-smooth" in arguments:
sigmas = arguments["-smooth"]
if len(sigmas) == 1:
sigmas = [sigmas[0] for i in range(len(data.shape))]
elif len(sigmas) != len(data.shape):
printv(parser.usage.generate(error='ERROR: -smooth need the same number of inputs as the number of image dimension OR only one input'))
# adjust sigma based on voxel size
sigmas = [sigmas[i] / dim[i+4] for i in range(3)]
# smooth data
data_out = smooth(data, sigmas)
elif '-dilate' in arguments:
data_out = dilate(data, arguments['-dilate'])
elif '-erode' in arguments:
data_out = erode(data, arguments['-erode'])
elif '-denoise' in arguments:
# parse denoising arguments
p, b = 1, 5 # default arguments
list_denoise = arguments['-denoise']
for i in list_denoise:
if 'p' in i:
p = int(i.split('=')[1])
if 'b' in i:
b = int(i.split('=')[1])
data_out = denoise_nlmeans(data, patch_radius=p, block_radius=b)
elif '-symmetrize' in arguments:
data_out = (data + data[range(data.shape[0]-1, -1, -1), :, :]) / float(2)
elif '-mi' in arguments:
# input 1 = from flag -i --> im
# input 2 = from flag -mi
im_2 = Image(arguments['-mi'])
compute_similarity(im.data, im_2.data, fname_out, metric='mi', verbose=verbose)
data_out=None
elif '-corr' in arguments:
# input 1 = from flag -i --> im
# input 2 = from flag -mi
im_2 = Image(arguments['-corr'])
compute_similarity(im.data, im_2.data, fname_out, metric='corr', verbose=verbose)
data_out=None
# if no flag is set
else:
data_out = None
printv(parser.usage.generate(error='ERROR: you need to specify an operation to do on the input image'))
if data_out is not None:
# Write output
nii_out = Image(fname_in) # use header of input file
nii_out.data = data_out
nii_out.setFileName(fname_out)
nii_out.save()
# TODO: case of multiple outputs
# assert len(data_out) == n_out
# if n_in == n_out:
# for im_in, d_out, fn_out in zip(nii, data_out, fname_out):
# im_in.data = d_out
# im_in.setFileName(fn_out)
# if "-w" in arguments:
# im_in.hdr.set_intent('vector', (), '')
# im_in.save()
# elif n_out == 1:
# nii[0].data = data_out[0]
# nii[0].setFileName(fname_out[0])
# if "-w" in arguments:
# nii[0].hdr.set_intent('vector', (), '')
# nii[0].save()
# elif n_out > n_in:
# for dat_out, name_out in zip(data_out, fname_out):
# im_out = nii[0].copy()
# im_out.data = dat_out
# im_out.setFileName(name_out)
# if "-w" in arguments:
# im_out.hdr.set_intent('vector', (), '')
# im_out.save()
# else:
# printv(parser.usage.generate(error='ERROR: not the correct numbers of inputs and outputs'))
# display message
if data_out is not None:
printv('\nDone! To view results, type:', verbose)
printv('fslview '+fname_out+' &\n', verbose, 'info')
else:
printv('\nDone! File created: '+fname_out, verbose, 'info')
