def set_orientation(im, orientation, data_inversion=False, filename=False, fname_out=''):
"""
Set orientation on image
:param im: either Image object or file name. Carefully set param filename.
:param orientation:
:param data_inversion:
:param filename:
:return:
"""
if fname_out:
pass
elif filename:
path, fname, ext = extract_fname(im)
fname_out = fname+'_'+orientation+ext
else:
fname_out = im.file_name+'_'+orientation+im.ext
if not data_inversion:
from sct_utils import run
if filename:
run('isct_orientation3d -i '+im+' -orientation '+orientation+' -o '+fname_out, 0)
im_out = fname_out
else:
run('isct_orientation3d -i '+im.absolutepath+' -orientation '+orientation+' -o '+fname_out, 0)
im_out = Image(fname_out)
else:
im_out = im.copy()
im_out.change_orientation(orientation, True)
im_out.setFileName(fname_out)
return im_out
def clean_labeled_segmentation(fname_labeled_seg, fname_seg, fname_labeled_seg_new):
"""
Clean labeled segmentation by:
(i) removing voxels in segmentation_labeled that are not in segmentation and
(ii) adding voxels in segmentation that are not in segmentation_labeled
:param fname_labeled_seg:
:param fname_seg:
:param fname_labeled_seg_new: output
:return: none
"""
# remove voxels in segmentation_labeled that are not in segmentation
run('sct_maths -i '+fname_labeled_seg+' -mul '+fname_seg+' -o segmentation_labeled_mul.nii.gz')
# add voxels in segmentation that are not in segmentation_labeled
run('sct_maths -i '+fname_labeled_seg+' -dilate 2 -o segmentation_labeled_dilate.nii.gz') # dilate labeled segmentation
data_label_dilate = Image('segmentation_labeled_dilate.nii.gz').data
run('sct_maths -i segmentation_labeled_mul.nii.gz -bin 0 -o segmentation_labeled_mul_bin.nii.gz')
data_label_bin = Image('segmentation_labeled_mul_bin.nii.gz').data
data_seg = Image(fname_seg).data
data_diff = data_seg - data_label_bin
ind_nonzero = np.where(data_diff)
im_label = Image('segmentation_labeled_mul.nii.gz')
for i_vox in range(len(ind_nonzero[0])):
# assign closest label value for this voxel
ix, iy, iz = ind_nonzero[0][i_vox], ind_nonzero[1][i_vox], ind_nonzero[2][i_vox]
im_label.data[ix, iy, iz] = data_label_dilate[ix, iy, iz]
# save new label file (overwrite)
im_label.setFileName(fname_labeled_seg_new)
im_label.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 remove_label(self, symmetry=False):
"""
Compare two label images and remove any labels in input image that are not in reference image.
The symmetry option enables to remove labels from reference image that are not in input image
"""
# image_output = Image(self.image_input.dim, orientation=self.image_input.orientation, hdr=self.image_input.hdr, verbose=self.verbose)
image_output = Image(self.image_input, verbose=self.verbose)
image_output.data *= 0 # put all voxels to 0
result_coord_input, result_coord_ref = self.remove_label_coord(self.image_input.getNonZeroCoordinates(coordValue=True),
self.image_ref.getNonZeroCoordinates(coordValue=True), symmetry)
for coord in result_coord_input:
image_output.data[int(coord.x), int(coord.y), int(coord.z)] = int(round(coord.value))
if symmetry:
# image_output_ref = Image(self.image_ref.dim, orientation=self.image_ref.orientation, hdr=self.image_ref.hdr, verbose=self.verbose)
image_output_ref = Image(self.image_ref, verbose=self.verbose)
for coord in result_coord_ref:
image_output_ref.data[int(coord.x), int(coord.y), int(coord.z)] = int(round(coord.value))
image_output_ref.setFileName(self.fname_output[1])
image_output_ref.save('minimize_int')
self.fname_output = self.fname_output[0]
return image_output
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 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 savePredictions(predictions, path_output, list_images, segmentation_image_size):
number_of_images = len(list_images)
predictions = numpy.reshape(predictions, [number_of_images, segmentation_image_size, segmentation_image_size, NUM_LABELS])
predictions = predictions[:, :, :, 1]
for i, pref in enumerate(predictions):
im_pred = Image(pref)
im_pred.setFileName(path_output+sct.add_suffix(list_images[i], '_pred'))
im_pred.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 set_orientation(fname_in, orientation, fname_out, inversion=False):
if not inversion:
sct.run('isct_orientation3d -i '+fname_in+' -orientation '+orientation+' -o '+fname_out, 0)
else:
from msct_image import Image
input_image = Image(fname_in)
input_image.change_orientation(orientation, True)
input_image.setFileName(fname_out)
input_image.save()
# return full path
return os.path.abspath(fname_out)
def get_im_from_list(self, data):
im = Image(data)
# set pix dimension
im.hdr.structarr['pixdim'][1] = self.param_data.axial_res
im.hdr.structarr['pixdim'][2] = self.param_data.axial_res
# set the correct orientation
im.setFileName('im_to_orient.nii.gz')
im.save()
im = set_orientation(im, 'IRP')
im = set_orientation(im, 'PIL', data_inversion=True)
return im
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 convert(fname_in, fname_out, squeeze_data=True, type=None, verbose=1):
"""
Convert data
:return True/False
"""
from msct_image import Image
from sct_utils import printv
printv('sct_convert -i '+fname_in+' -o '+fname_out, verbose, 'code')
# Open file
im = Image(fname_in)
# Save file
im.setFileName(fname_out)
if type is not None:
im.changeType(type=type)
im.save(squeeze_data=squeeze_data)
return im
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 remove_overlap(file_centerline_generated_by_labels, file_with_seg_or_centerline, output_file_name, parameter=0):
# Image file process
if parameter == 0 :
image_with_seg_or_centerline = Image(file_with_seg_or_centerline).copy()
z_test = ComputeZMinMax(image_with_seg_or_centerline)
zmax = z_test.Zmax
zmin = z_test.Zmin
tab1 = Image(file_centerline_generated_by_labels).copy()
size_x=tab1.data.shape[0]
size_y=tab1.data.shape[1]
#X_coor, Y_coor, Z_coor = (tab1.data).nonzero()
#nb_one = X_coor.shape[0]
#for i in range(0, nb_one):
# tab1.data[X_coor[i], Y_coor[i], X_coor[i]] = 0
#each slice under zmax is filled with zeros
print zmax
for i in range(zmin, zmax):
tab1.data[:,:,i] = np.zeros((size_x,size_y))
#Save file
tab1.setFileName(output_file_name)
tab1.save('minimize') #size of image should be minimized
# Text file process
if parameter == 1 :
z_test = ComputeZMinMax(file_with_seg_or_centerline)
zmax = z_test.Zmax
zmin = z_test.Zmin
print zmax
#create output txt file
tab2 = open(output_file_name , "w")
tab2.close()
#Delete lines under zmax
with open(file_centerline_generated_by_labels) as f:
data_line = f.readlines()
with open(output_file_name, "w") as f1:
for line in data_line:
words = line.split()
if int(words [0]) < zmin or int(words [0]) > zmax :
f1.write(line)
def visualize_warp(fname_warp, fname_grid=None, step=3, rm_tmp=True):
if fname_grid is None:
from numpy import zeros
tmp_dir = sct.tmp_create()
im_warp = Image(fname_warp)
os.chdir(tmp_dir)
assert len(im_warp.data.shape) == 5, "ERROR: Warping field does bot have 5 dimensions..."
nx, ny, nz, nt, ndimwarp = im_warp.data.shape
# nx, ny, nz, nt, px, py, pz, pt = im_warp.dim
# This does not work because dimensions of a warping field are not correctly read : it would be 1,1,1,1,1,1,1,1
sq = zeros((step, step))
sq[step - 1] = 1
sq[:, step - 1] = 1
dat = zeros((nx, ny, nz))
for i in range(0, dat.shape[0], step):
for j in range(0, dat.shape[1], step):
for k in range(dat.shape[2]):
if dat[i : i + step, j : j + step, k].shape == (step, step):
dat[i : i + step, j : j + step, k] = sq
fname_grid = "grid_" + str(step) + ".nii.gz"
im_grid = Image(param=dat)
grid_hdr = im_warp.hdr
im_grid.hdr = grid_hdr
im_grid.setFileName(fname_grid)
im_grid.save()
fname_grid_resample = sct.add_suffix(fname_grid, "_resample")
sct.run("sct_resample -i " + fname_grid + " -f 3x3x1 -x nn -o " + fname_grid_resample)
fname_grid = tmp_dir + fname_grid_resample
os.chdir("..")
path_warp, file_warp, ext_warp = sct.extract_fname(fname_warp)
grid_warped = path_warp + "grid_warped_gm" + ext_warp
sct.run("sct_apply_transfo -i " + fname_grid + " -d " + fname_grid + " -w " + fname_warp + " -o " + grid_warped)
if rm_tmp:
sct.run("rm -rf " + tmp_dir, error_exit="warning")
return grid_warped
def visualize_warp(fname_warp, fname_grid=None, step=3, rm_tmp=True):
if fname_grid is None:
from numpy import zeros
tmp_dir = tmp_create()
im_warp = Image(fname_warp)
status, out = run('fslhd '+fname_warp)
from os import chdir
chdir(tmp_dir)
dim1 = 'dim1 '
dim2 = 'dim2 '
dim3 = 'dim3 '
nx = int(out[out.find(dim1):][len(dim1):out[out.find(dim1):].find('\n')])
ny = int(out[out.find(dim2):][len(dim2):out[out.find(dim2):].find('\n')])
nz = int(out[out.find(dim3):][len(dim3):out[out.find(dim3):].find('\n')])
sq = zeros((step, step))
sq[step-1] = 1
sq[:, step-1] = 1
dat = zeros((nx, ny, nz))
for i in range(0, dat.shape[0], step):
for j in range(0, dat.shape[1], step):
for k in range(dat.shape[2]):
if dat[i:i+step, j:j+step, k].shape == (step, step):
dat[i:i+step, j:j+step, k] = sq
fname_grid = 'grid_'+str(step)+'.nii.gz'
im_grid = Image(param=dat)
grid_hdr = im_warp.hdr
im_grid.hdr = grid_hdr
im_grid.setFileName(fname_grid)
im_grid.save()
fname_grid_resample = add_suffix(fname_grid, '_resample')
run('sct_resample -i '+fname_grid+' -f 3x3x1 -x nn -o '+fname_grid_resample)
fname_grid = tmp_dir+fname_grid_resample
chdir('..')
path_warp, file_warp, ext_warp = extract_fname(fname_warp)
grid_warped = path_warp+extract_fname(fname_grid)[1]+'_'+file_warp+ext_warp
run('sct_apply_transfo -i '+fname_grid+' -d '+fname_grid+' -w '+fname_warp+' -o '+grid_warped)
if rm_tmp:
run('rm -rf '+tmp_dir, error_exit='warning')
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 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)
# print arguments
print '\nCheck parameters:'
print ' 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:
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 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(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(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 '-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)
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()
# 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')
def main():
parser = get_parser()
param = Param()
arguments = parser.parse(sys.argv[1:])
# get arguments
fname_data = arguments['-i']
fname_seg = arguments['-s']
fname_landmarks = arguments['-l']
if '-ofolder' in arguments:
path_output = arguments['-ofolder']
else:
path_output = ''
path_template = sct.slash_at_the_end(arguments['-t'], 1)
contrast_template = arguments['-c']
remove_temp_files = int(arguments['-r'])
verbose = int(arguments['-v'])
if '-param-straighten' in arguments:
param.param_straighten = arguments['-param-straighten']
if 'cpu-nb' in arguments:
arg_cpu = ' -cpu-nb '+arguments['-cpu-nb']
else:
arg_cpu = ''
if '-param' in arguments:
paramreg_user = arguments['-param']
# update registration parameters
for paramStep in paramreg_user:
paramreg.addStep(paramStep)
# initialize other parameters
file_template_label = param.file_template_label
output_type = param.output_type
zsubsample = param.zsubsample
# smoothing_sigma = param.smoothing_sigma
# capitalize letters for contrast
if contrast_template == 't1':
contrast_template = 'T1'
elif contrast_template == 't2':
contrast_template = 'T2'
# retrieve file_template based on contrast
fname_template_list = glob(path_template+param.folder_template+'*'+contrast_template+'.nii.gz')
# TODO: make sure there is only one file -- check if file is there otherwise it crashes
fname_template = fname_template_list[0]
# retrieve file_template_seg
fname_template_seg_list = glob(path_template+param.folder_template+'*cord.nii.gz')
# TODO: make sure there is only one file
fname_template_seg = fname_template_seg_list[0]
# start timer
start_time = time.time()
# get absolute path - TO DO: remove! NEVER USE ABSOLUTE PATH...
path_template = os.path.abspath(path_template+param.folder_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('.. Path output: '+path_output, 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 correspondence in template space. \nLabel max '
'provided: ' + str(labels[-1].value) + '\nLabel max from template: ' +
str(labels_template[-1].value), verbose, 'error')
# create temporary folder
path_tmp = sct.tmp_create(verbose=verbose)
# set temporary file names
ftmp_data = 'data.nii'
ftmp_seg = 'seg.nii.gz'
ftmp_label = 'label.nii.gz'
ftmp_template = 'template.nii'
ftmp_template_seg = 'template_seg.nii.gz'
ftmp_template_label = 'template_label.nii.gz'
# copy files to temporary folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
sct.run('sct_convert -i '+fname_data+' -o '+path_tmp+ftmp_data)
sct.run('sct_convert -i '+fname_seg+' -o '+path_tmp+ftmp_seg)
sct.run('sct_convert -i '+fname_landmarks+' -o '+path_tmp+ftmp_label)
sct.run('sct_convert -i '+fname_template+' -o '+path_tmp+ftmp_template)
sct.run('sct_convert -i '+fname_template_seg+' -o '+path_tmp+ftmp_template_seg)
sct.run('sct_convert -i '+fname_template_label+' -o '+path_tmp+ftmp_template_label)
# go to tmp folder
os.chdir(path_tmp)
# smooth segmentation (jcohenadad, issue #613)
sct.printv('\nSmooth segmentation...', verbose)
sct.run('sct_maths -i '+ftmp_seg+' -smooth 1.5 -o '+add_suffix(ftmp_seg, '_smooth'))
ftmp_seg = add_suffix(ftmp_seg, '_smooth')
# resample data to 1mm isotropic
sct.printv('\nResample data to 1mm isotropic...', verbose)
sct.run('sct_resample -i '+ftmp_data+' -mm 1.0x1.0x1.0 -x linear -o '+add_suffix(ftmp_data, '_1mm'))
ftmp_data = add_suffix(ftmp_data, '_1mm')
sct.run('sct_resample -i '+ftmp_seg+' -mm 1.0x1.0x1.0 -x linear -o '+add_suffix(ftmp_seg, '_1mm'))
ftmp_seg = add_suffix(ftmp_seg, '_1mm')
# 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(ftmp_label, ftmp_data, add_suffix(ftmp_label, '_1mm'))
ftmp_label = add_suffix(ftmp_label, '_1mm')
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
sct.run('sct_image -i '+ftmp_data+' -setorient RPI -o '+add_suffix(ftmp_data, '_rpi'))
ftmp_data = add_suffix(ftmp_data, '_rpi')
sct.run('sct_image -i '+ftmp_seg+' -setorient RPI -o '+add_suffix(ftmp_seg, '_rpi'))
ftmp_seg = add_suffix(ftmp_seg, '_rpi')
sct.run('sct_image -i '+ftmp_label+' -setorient RPI -o '+add_suffix(ftmp_label, '_rpi'))
ftmp_label = add_suffix(ftmp_label, '_rpi')
# get landmarks in native space
# crop segmentation
# output: segmentation_rpi_crop.nii.gz
status_crop, output_crop = sct.run('sct_crop_image -i '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_crop')+' -dim 2 -bzmax', verbose)
ftmp_seg = add_suffix(ftmp_seg, '_crop')
cropping_slices = output_crop.split('Dimension 2: ')[1].split('\n')[0].split(' ')
# straighten segmentation
sct.printv('\nStraighten the spinal cord using centerline/segmentation...', verbose)
sct.run('sct_straighten_spinalcord -i '+ftmp_seg+' -s '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_straight')+' -qc 0 -r 0 -v '+str(verbose)+' '+param.param_straighten+arg_cpu, verbose)
# N.B. DO NOT UPDATE VARIABLE ftmp_seg BECAUSE TEMPORARY USED LATER
# re-define warping field using non-cropped space (to avoid issue #367)
sct.run('sct_concat_transfo -w warp_straight2curve.nii.gz -d '+ftmp_data+' -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 -p remove -i '+ftmp_template_label+' -o '+ftmp_template_label+' -r '+ftmp_label)
# Dilating the input label so they can be straighten without losing them
sct.printv('\nDilating input labels using 3vox ball radius')
sct.run('sct_maths -i '+ftmp_label+' -o '+add_suffix(ftmp_label, '_dilate')+' -dilate 3')
ftmp_label = add_suffix(ftmp_label, '_dilate')
# Apply straightening to labels
sct.printv('\nApply straightening to labels...', verbose)
sct.run('sct_apply_transfo -i '+ftmp_label+' -o '+add_suffix(ftmp_label, '_straight')+' -d '+add_suffix(ftmp_seg, '_straight')+' -w warp_curve2straight.nii.gz -x nn')
ftmp_label = add_suffix(ftmp_label, '_straight')
# Create crosses for the template labels and get coordinates
sct.printv('\nCreate a 15 mm cross for the template labels...', verbose)
template_image = Image(ftmp_template_label)
coordinates_input = template_image.getNonZeroCoordinates(sorting='value')
# jcohenadad, issue #628 <<<<<
# landmark_template = ProcessLabels.get_crosses_coordinates(coordinates_input, gapxy=15)
landmark_template = coordinates_input
# >>>>>
if verbose == 2:
# TODO: assign cross to image before saving
template_image.setFileName(add_suffix(ftmp_template_label, '_cross'))
template_image.save(type='minimize_int')
# Create crosses for the input labels into straight space and get coordinates
sct.printv('\nCreate a 15 mm cross for the input labels...', verbose)
label_straight_image = Image(ftmp_label)
coordinates_input = label_straight_image.getCoordinatesAveragedByValue() # landmarks are sorted by value
# jcohenadad, issue #628 <<<<<
# landmark_straight = ProcessLabels.get_crosses_coordinates(coordinates_input, gapxy=15)
landmark_straight = coordinates_input
# >>>>>
if verbose == 2:
# TODO: assign cross to image before saving
label_straight_image.setFileName(add_suffix(ftmp_label, '_cross'))
label_straight_image.save(type='minimize_int')
# Reorganize landmarks
points_fixed, points_moving = [], []
for coord in landmark_straight:
point_straight = label_straight_image.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 = template_image.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
# for some reason, the moving and fixed points are inverted between ITK transform and our python-based transform.
# and for another unknown reason, x and y dimensions have a negative sign (at least for translation and center of rotation).
if verbose == 2:
show_transfo = True
else:
show_transfo = False
(rotation_matrix, translation_array, points_moving_reg, points_moving_barycenter) = msct_register_landmarks.getRigidTransformFromLandmarks(points_moving, points_fixed, constraints='translation-scaling-z', show=show_transfo)
# 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: AffineTransform_double_3_3\n")
text_file.write("Parameters: %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f\n" % (
rotation_matrix[0, 0], rotation_matrix[0, 1], rotation_matrix[0, 2],
rotation_matrix[1, 0], rotation_matrix[1, 1], rotation_matrix[1, 2],
rotation_matrix[2, 0], rotation_matrix[2, 1], 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()
# Concatenate transformations: curve --> straight --> affine
sct.printv('\nConcatenate transformations: curve --> straight --> affine...', verbose)
sct.run('sct_concat_transfo -w warp_curve2straight.nii.gz,straight2templateAffine.txt -d template.nii -o warp_curve2straightAffine.nii.gz')
# Apply transformation
sct.printv('\nApply transformation...', verbose)
sct.run('sct_apply_transfo -i '+ftmp_data+' -o '+add_suffix(ftmp_data, '_straightAffine')+' -d '+ftmp_template+' -w warp_curve2straightAffine.nii.gz')
ftmp_data = add_suffix(ftmp_data, '_straightAffine')
sct.run('sct_apply_transfo -i '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_straightAffine')+' -d '+ftmp_template+' -w warp_curve2straightAffine.nii.gz -x linear')
ftmp_seg = add_suffix(ftmp_seg, '_straightAffine')
# threshold and binarize
sct.printv('\nBinarize segmentation...', verbose)
sct.run('sct_maths -i '+ftmp_seg+' -thr 0.4 -o '+add_suffix(ftmp_seg, '_thr'))
sct.run('sct_maths -i '+add_suffix(ftmp_seg, '_thr')+' -bin -o '+add_suffix(ftmp_seg, '_thr_bin'))
ftmp_seg = add_suffix(ftmp_seg, '_thr_bin')
# find min-max of anat2template (for subsequent cropping)
zmin_template, zmax_template = find_zmin_zmax(ftmp_seg)
# 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 '+ftmp_template+' -o '+add_suffix(ftmp_template, '_crop')+' -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
ftmp_template = add_suffix(ftmp_template, '_crop')
sct.run('sct_crop_image -i '+ftmp_template_seg+' -o '+add_suffix(ftmp_template_seg, '_crop')+' -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
ftmp_template_seg = add_suffix(ftmp_template_seg, '_crop')
sct.run('sct_crop_image -i '+ftmp_data+' -o '+add_suffix(ftmp_data, '_crop')+' -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
ftmp_data = add_suffix(ftmp_data, '_crop')
sct.run('sct_crop_image -i '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_crop')+' -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
ftmp_seg = add_suffix(ftmp_seg, '_crop')
# sub-sample in z-direction
sct.printv('\nSub-sample in z-direction (for faster processing)...', verbose)
sct.run('sct_resample -i '+ftmp_template+' -o '+add_suffix(ftmp_template, '_sub')+' -f 1x1x'+zsubsample, verbose)
ftmp_template = add_suffix(ftmp_template, '_sub')
sct.run('sct_resample -i '+ftmp_template_seg+' -o '+add_suffix(ftmp_template_seg, '_sub')+' -f 1x1x'+zsubsample, verbose)
ftmp_template_seg = add_suffix(ftmp_template_seg, '_sub')
sct.run('sct_resample -i '+ftmp_data+' -o '+add_suffix(ftmp_data, '_sub')+' -f 1x1x'+zsubsample, verbose)
ftmp_data = add_suffix(ftmp_data, '_sub')
sct.run('sct_resample -i '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_sub')+' -f 1x1x'+zsubsample, verbose)
ftmp_seg = add_suffix(ftmp_seg, '_sub')
# Registration straight spinal cord to template
sct.printv('\nRegister straight spinal cord to template...', verbose)
# loop across registration steps
warp_forward = []
warp_inverse = []
for i_step in range(1, len(paramreg.steps)+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 = ftmp_data
dest = ftmp_template
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_seg
dest = ftmp_template_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# if 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 '+add_suffix(src, '_reg')+' -x '+interp_step, verbose)
src = 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)
sct.printv('\nConcatenate transformations: template --> anat...', 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 -crop 1', verbose)
sct.run('sct_apply_transfo -i data.nii -o anat2template.nii.gz -d template.nii -w warp_anat2template.nii.gz -crop 1', verbose)
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(path_tmp+'warp_template2anat.nii.gz', path_output+'warp_template2anat.nii.gz', verbose)
sct.generate_output_file(path_tmp+'warp_anat2template.nii.gz', path_output+'warp_anat2template.nii.gz', verbose)
if output_type == 1:
sct.generate_output_file(path_tmp+'template2anat.nii.gz', path_output+'template2anat'+ext_data, verbose)
sct.generate_output_file(path_tmp+'anat2template.nii.gz', path_output+'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+' '+path_output+'template2anat -b 0,4000 &', verbose, 'info')
sct.printv('fslview '+fname_template+' -b 0,5000 '+path_output+'anat2template &\n', verbose, 'info')
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 validation(self, fname_manual_gmseg, fname_sc_seg):
path_manual_gmseg, file_manual_gmseg, ext_manual_gmseg = sct.extract_fname(
fname_manual_gmseg)
path_sc_seg, file_sc_seg, ext_sc_seg = sct.extract_fname(fname_sc_seg)
# Create tmp folder and copy files in it
tmp_dir = sct.tmp_create()
sct.run('cp ' + fname_manual_gmseg + ' ' + tmp_dir +
file_manual_gmseg + ext_manual_gmseg)
sct.run('cp ' + fname_sc_seg + ' ' + tmp_dir + file_sc_seg +
ext_sc_seg)
sct.run('cp ' + self.param.output_folder +
self.fname_warp_template2gm + ' ' + tmp_dir +
self.fname_warp_template2gm)
os.chdir(tmp_dir)
sct.run('sct_warp_template -d ' + fname_manual_gmseg + ' -w ' +
self.fname_warp_template2gm + ' -qc 0 -a 0')
if 'MNI-Poly-AMU_GM.nii.gz' in os.listdir('label/template/'):
im_new_template_gm = Image('label/template/MNI-Poly-AMU_GM.nii.gz')
im_new_template_wm = Image('label/template/MNI-Poly-AMU_WM.nii.gz')
else:
im_new_template_gm = Image('label/template/PAM50_gm.nii.gz')
im_new_template_wm = Image('label/template/PAM50_wm.nii.gz')
im_new_template_gm = thr_im(im_new_template_gm, self.param.thr,
self.param.thr)
im_new_template_wm = thr_im(im_new_template_wm, self.param.thr,
self.param.thr)
self.im_template_gm = thr_im(self.im_template_gm, self.param.thr,
self.param.thr)
self.im_template_wm = thr_im(self.im_template_wm, self.param.thr,
self.param.thr)
fname_new_template_gm = 'new_template_gm.nii.gz'
im_new_template_gm.setFileName(fname_new_template_gm)
im_new_template_gm.save()
fname_new_template_wm = 'new_template_wm.nii.gz'
im_new_template_wm.setFileName(fname_new_template_wm)
im_new_template_wm.save()
fname_old_template_wm = 'old_template_wm.nii.gz'
self.im_template_wm.setFileName(fname_old_template_wm)
self.im_template_wm.save()
fname_old_template_gm = 'old_template_gm.nii.gz'
self.im_template_gm.setFileName(fname_old_template_gm)
self.im_template_gm.save()
fname_manual_wmseg = 'target_manual_wmseg.nii.gz'
sct.run('sct_maths -i ' + file_sc_seg + ext_sc_seg + ' -sub ' +
file_manual_gmseg + ext_manual_gmseg + ' -o ' +
fname_manual_wmseg)
# Compute Hausdorff distance
status, output_old_hd = sct.run('sct_compute_hausdorff_distance -i ' +
fname_old_template_gm + ' -r ' +
file_manual_gmseg + ext_manual_gmseg +
' -t 1 -v 1')
status, output_new_hd = sct.run('sct_compute_hausdorff_distance -i ' +
fname_new_template_gm + ' -r ' +
file_manual_gmseg + ext_manual_gmseg +
' -t 1 -v 1')
hd_name = 'hd_md_multilabel_reg.txt'
hd_fic = open(hd_name, 'w')
hd_fic.write(
'The "diff" columns are comparisons between regular template registration and corrected template registration according to SC internal structure\n'
'Diff = metric_regular_reg - metric_corrected_reg\n')
hd_fic.write('#Slice, HD, HD diff, MD, MD diff\n')
no_ref_slices = []
init_hd = "Hausdorff's distance - First relative Hausdorff's distance median - Second relative Hausdorff's distance median(all in mm)\n"
old_gm_hd = output_old_hd[output_old_hd.find(init_hd) +
len(init_hd):].split('\n')
new_gm_hd = output_new_hd[output_new_hd.find(init_hd) +
len(init_hd):].split('\n')
for i in range(len(old_gm_hd) - 3): # last two lines are informations
i_old, val_old = old_gm_hd[i].split(':')
i_new, val_new = new_gm_hd[i].split(':')
i_old = int(i_old[-2:])
i_new = int(i_new[-2:])
assert i == i_old == i_new, 'ERROR: when comparing Hausdorff distances, slice numbers differs.'
hd_old, med1_old, med2_old = val_old.split('-')
hd_new, med1_new, med2_new = val_new.split('-')
if float(hd_old) == 0.0:
no_ref_slices.append(i)
hd_fic.write(str(i) + ', NO MANUAL SEGMENTATION\n')
else:
md_new = max(float(med1_new), float(med2_new))
md_old = max(float(med1_old), float(med2_old))
hd_fic.write(
str(i) + ', ' + hd_new + ', ' +
str(float(hd_old) - float(hd_new)) + ', ' + str(md_new) +
', ' + str(md_old - md_new) + '\n')
hd_fic.close()
# Compute Dice coefficient
# --- DC old template
try:
status_old_gm, output_old_gm = sct.run(
'sct_dice_coefficient -i ' + file_manual_gmseg +
ext_manual_gmseg + ' -d ' + fname_old_template_gm +
' -2d-slices 2',
error_exit='warning',
raise_exception=True)
except Exception:
# put the result and the reference in the same space using a registration with ANTs with no iteration:
corrected_manual_gmseg = file_manual_gmseg + '_in_old_template_space' + ext_manual_gmseg
sct.run('isct_antsRegistration -d 3 -t Translation[0] -m MI[' +
fname_old_template_gm + ',' + file_manual_gmseg +
ext_manual_gmseg + ',1,16] -o [reg_ref_to_res,' +
corrected_manual_gmseg + '] -n BSpline[3] -c 0 -f 1 -s 0')
# sct.run('sct_maths -i '+corrected_manual_gmseg+' -thr 0.1 -o '+corrected_manual_gmseg)
sct.run('sct_maths -i ' + corrected_manual_gmseg +
' -bin 0.1 -o ' + corrected_manual_gmseg)
status_old_gm, output_old_gm = sct.run(
'sct_dice_coefficient -i ' + corrected_manual_gmseg + ' -d ' +
fname_old_template_gm + ' -2d-slices 2',
error_exit='warning')
try:
status_old_wm, output_old_wm = sct.run(
'sct_dice_coefficient -i ' + fname_manual_wmseg + ' -d ' +
fname_old_template_wm + ' -2d-slices 2',
error_exit='warning',
raise_exception=True)
except Exception:
# put the result and the reference in the same space using a registration with ANTs with no iteration:
path_manual_wmseg, file_manual_wmseg, ext_manual_wmseg = sct.extract_fname(
fname_manual_wmseg)
corrected_manual_wmseg = file_manual_wmseg + '_in_old_template_space' + ext_manual_wmseg
sct.run('isct_antsRegistration -d 3 -t Translation[0] -m MI[' +
fname_old_template_wm + ',' + fname_manual_wmseg +
',1,16] -o [reg_ref_to_res,' + corrected_manual_wmseg +
'] -n BSpline[3] -c 0 -f 1 -s 0')
# sct.run('sct_maths -i '+corrected_manual_wmseg+' -thr 0.1 -o '+corrected_manual_wmseg)
sct.run('sct_maths -i ' + corrected_manual_wmseg +
' -bin 0.1 -o ' + corrected_manual_wmseg)
status_old_wm, output_old_wm = sct.run(
'sct_dice_coefficient -i ' + corrected_manual_wmseg + ' -d ' +
fname_old_template_wm + ' -2d-slices 2',
error_exit='warning')
# --- DC new template
try:
status_new_gm, output_new_gm = sct.run(
'sct_dice_coefficient -i ' + file_manual_gmseg +
ext_manual_gmseg + ' -d ' + fname_new_template_gm +
' -2d-slices 2',
error_exit='warning',
raise_exception=True)
except Exception:
# put the result and the reference in the same space using a registration with ANTs with no iteration:
corrected_manual_gmseg = file_manual_gmseg + '_in_new_template_space' + ext_manual_gmseg
sct.run('isct_antsRegistration -d 3 -t Translation[0] -m MI[' +
fname_new_template_gm + ',' + file_manual_gmseg +
ext_manual_gmseg + ',1,16] -o [reg_ref_to_res,' +
corrected_manual_gmseg + '] -n BSpline[3] -c 0 -f 1 -s 0')
# sct.run('sct_maths -i '+corrected_manual_gmseg+' -thr 0.1 -o '+corrected_manual_gmseg)
sct.run('sct_maths -i ' + corrected_manual_gmseg +
' -bin 0.1 -o ' + corrected_manual_gmseg)
status_new_gm, output_new_gm = sct.run(
'sct_dice_coefficient -i ' + corrected_manual_gmseg + ' -d ' +
fname_new_template_gm + ' -2d-slices 2',
error_exit='warning')
try:
status_new_wm, output_new_wm = sct.run(
'sct_dice_coefficient -i ' + fname_manual_wmseg + ' -d ' +
fname_new_template_wm + ' -2d-slices 2',
error_exit='warning',
raise_exception=True)
except Exception:
# put the result and the reference in the same space using a registration with ANTs with no iteration:
path_manual_wmseg, file_manual_wmseg, ext_manual_wmseg = sct.extract_fname(
fname_manual_wmseg)
corrected_manual_wmseg = file_manual_wmseg + '_in_new_template_space' + ext_manual_wmseg
sct.run('isct_antsRegistration -d 3 -t Translation[0] -m MI[' +
fname_new_template_wm + ',' + fname_manual_wmseg +
',1,16] -o [reg_ref_to_res,' + corrected_manual_wmseg +
'] -n BSpline[3] -c 0 -f 1 -s 0')
# sct.run('sct_maths -i '+corrected_manual_wmseg+' -thr 0.1 -o '+corrected_manual_wmseg)
sct.run('sct_maths -i ' + corrected_manual_wmseg +
' -bin 0.1 -o ' + corrected_manual_wmseg)
status_new_wm, output_new_wm = sct.run(
'sct_dice_coefficient -i ' + corrected_manual_wmseg + ' -d ' +
fname_new_template_wm + ' -2d-slices 2',
error_exit='warning')
dice_name = 'dice_multilabel_reg.txt'
dice_fic = open(dice_name, 'w')
dice_fic.write(
'The "diff" columns are comparisons between regular template registration and corrected template registration according to SC internal structure\n'
'Diff = metric_corrected_reg - metric_regular_reg\n')
dice_fic.write('#Slice, WM DC, WM diff, GM DC, GM diff\n')
init_dc = '2D Dice coefficient by slice:\n'
old_gm_dc = output_old_gm[output_old_gm.find(init_dc) +
len(init_dc):].split('\n')
old_wm_dc = output_old_wm[output_old_wm.find(init_dc) +
len(init_dc):].split('\n')
new_gm_dc = output_new_gm[output_new_gm.find(init_dc) +
len(init_dc):].split('\n')
new_wm_dc = output_new_wm[output_new_wm.find(init_dc) +
len(init_dc):].split('\n')
for i in range(len(old_gm_dc)):
if i not in no_ref_slices:
i_new_gm, val_new_gm = new_gm_dc[i].split(' ')
i_new_wm, val_new_wm = new_wm_dc[i].split(' ')
i_old_gm, val_old_gm = old_gm_dc[i].split(' ')
i_old_wm, val_old_wm = old_wm_dc[i].split(' ')
assert i == int(i_new_gm) == int(i_new_wm) == int(
i_old_gm
) == int(
i_old_wm
), 'ERROR: when comparing Dice coefficients, slice numbers differs.'
dice_fic.write(
str(i) + ', ' + val_new_wm + ', ' +
str(float(val_new_wm) - float(val_old_wm)) + ', ' +
val_new_gm + ', ' +
str(float(val_new_gm) - float(val_old_gm)) + '\n')
else:
dice_fic.write(str(i) + ', NO MANUAL SEGMENTATION\n')
dice_fic.close()
os.chdir('..')
sct.generate_output_file(tmp_dir + hd_name,
self.param.output_folder + hd_name)
sct.generate_output_file(tmp_dir + dice_name,
self.param.output_folder + dice_name)
if self.param.remove_tmp:
sct.run('rm -rf ' + tmp_dir, error_exit='warning')
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 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 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')
#!/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 main(args=None):
# initializations
output_type = ''
param = Param()
dim_list = ['x', 'y', 'z', 't']
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(args)
fname_in = arguments["-i"]
n_in = len(fname_in)
verbose = int(arguments['-v'])
if "-o" in arguments:
fname_out = arguments["-o"]
else:
fname_out = None
# Open file(s)
# im_in_list = [Image(fn) for fn in fname_in]
# run command
if "-concat" in arguments:
dim = arguments["-concat"]
assert dim in dim_list
dim = dim_list.index(dim)
im_out = [concat_data(fname_in, dim)] # TODO: adapt to fname_in
elif "-copy-header" in arguments:
im_in = Image(fname_in[0])
im_dest = Image(arguments["-copy-header"])
im_out = [copy_header(im_in, im_dest)]
elif '-display-warp' in arguments:
im_in = fname_in[0]
visualize_warp(im_in, fname_grid=None, step=3, rm_tmp=True)
im_out = None
elif "-getorient" in arguments:
im_in = Image(fname_in[0])
orient = orientation(im_in, get=True, verbose=verbose)
im_out = None
elif '-keep-vol' in arguments:
index_vol = arguments['-keep-vol']
im_in = Image(fname_in[0])
im_out = [remove_vol(im_in, index_vol, todo='keep')]
elif '-mcs' in arguments:
im_in = Image(fname_in[0])
if n_in != 1:
printv(
parser.usage.generate(error='ERROR: -mcs need only one input'))
if len(im_in.data.shape) != 5:
printv(
parser.usage.generate(
error='ERROR: -mcs input need to be a multi-component image'
))
im_out = multicomponent_split(im_in)
elif '-omc' in arguments:
im_ref = Image(fname_in[0])
for fname in fname_in:
im = Image(fname)
if im.data.shape != im_ref.data.shape:
printv(
parser.usage.generate(
error=
'ERROR: -omc inputs need to have all the same shapes'))
del im
im_out = [multicomponent_merge(fname_in)] # TODO: adapt to fname_in
elif "-pad" in arguments:
im_in = Image(fname_in[0])
ndims = len(im_in.getDataShape())
if ndims != 3:
printv('ERROR: you need to specify a 3D input file.', 1, 'error')
return
pad_arguments = arguments["-pad"].split(',')
if len(pad_arguments) != 3:
printv('ERROR: you need to specify 3 padding values.', 1, 'error')
padx, pady, padz = pad_arguments
padx, pady, padz = int(padx), int(pady), int(padz)
im_out = [
pad_image(im_in,
pad_x_i=padx,
pad_x_f=padx,
pad_y_i=pady,
pad_y_f=pady,
pad_z_i=padz,
pad_z_f=padz)
]
elif "-pad-asym" in arguments:
im_in = Image(fname_in[0])
ndims = len(im_in.getDataShape())
if ndims != 3:
printv('ERROR: you need to specify a 3D input file.', 1, 'error')
return
pad_arguments = arguments["-pad-asym"].split(',')
if len(pad_arguments) != 6:
printv('ERROR: you need to specify 6 padding values.', 1, 'error')
padxi, padxf, padyi, padyf, padzi, padzf = pad_arguments
padxi, padxf, padyi, padyf, padzi, padzf = int(padxi), int(padxf), int(
padyi), int(padyf), int(padzi), int(padzf)
im_out = [
pad_image(im_in,
pad_x_i=padxi,
pad_x_f=padxf,
pad_y_i=padyi,
pad_y_f=padyf,
pad_z_i=padzi,
pad_z_f=padzf)
]
elif '-remove-vol' in arguments:
index_vol = arguments['-remove-vol']
im_in = Image(fname_in[0])
im_out = [remove_vol(im_in, index_vol, todo='remove')]
elif "-setorient" in arguments:
sct.printv(fname_in[0])
im_in = Image(fname_in[0])
im_out = [
orientation(im_in,
ori=arguments["-setorient"],
set=True,
verbose=verbose,
fname_out=fname_out)
]
elif "-setorient-data" in arguments:
im_in = Image(fname_in[0])
im_out = [
orientation(im_in,
ori=arguments["-setorient-data"],
set_data=True,
verbose=verbose)
]
elif "-split" in arguments:
dim = arguments["-split"]
assert dim in dim_list
im_in = Image(fname_in[0])
dim = dim_list.index(dim)
im_out = split_data(im_in, dim)
elif '-type' in arguments:
output_type = arguments['-type']
im_in = Image(fname_in[0])
im_out = [im_in] # TODO: adapt to fname_in
else:
im_out = None
printv(
parser.usage.generate(
error=
'ERROR: you need to specify an operation to do on the input image'
))
# in case fname_out is not defined, use first element of input file name list
if fname_out == None:
fname_out = fname_in[0]
# Write output
if im_out is not None:
printv('Generate output files...', verbose)
# if only one output
if len(im_out) == 1 and not '-split' in arguments:
im_out[0].setFileName(fname_out) if fname_out is not None else None
im_out[0].save(squeeze_data=False,
type=output_type,
verbose=verbose)
sct.display_viewer_syntax([fname_out], verbose=verbose)
if '-mcs' in arguments:
# use input file name and add _X, _Y _Z. Keep the same extension
fname_out = []
for i_dim in range(3):
fname_out.append(
add_suffix(fname_in[0], '_' + dim_list[i_dim].upper()))
im_out[i_dim].setFileName(fname_out[i_dim])
im_out[i_dim].save(verbose=verbose)
sct.display_viewer_syntax(fname_out)
if '-split' in arguments:
# use input file name and add _"DIM+NUMBER". Keep the same extension
l_fname_out = []
for i, im in enumerate(im_out):
l_fname_out.append(
add_suffix(fname_in[0],
'_' + dim_list[dim].upper() + str(i).zfill(4)))
im.setFileName(l_fname_out[i])
im.save(verbose=verbose)
sct.display_viewer_syntax(l_fname_out)
elif "-getorient" in arguments:
sct.printv(orient)
elif '-display-warp' in arguments:
printv('Warping grid generated.', verbose, 'info')
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 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():
# 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')
zsum = np.sum(np.sum(data_cord, 0), 0)
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
def interpolate_im_to_ref(im_input, im_input_sc, new_res=0.3, sq_size_size_mm=22.5, interpolation_mode=3):
nx, ny, nz, nt, px, py, pz, pt = im_input.dim
im_input_sc = im_input_sc.copy()
im_input= im_input.copy()
# keep only spacing and origin in qform to avoid rotation issues
input_qform = im_input.hdr.get_qform()
for i in range(4):
for j in range(4):
if i!=j and j!=3:
input_qform[i, j] = 0
im_input.hdr.set_qform(input_qform)
im_input.hdr.set_sform(input_qform)
im_input_sc.hdr = im_input.hdr
sq_size = int(sq_size_size_mm/new_res)
# create a reference image : square of ones
im_ref = Image(np.ones((sq_size, sq_size, 1), dtype=np.int), dim=(sq_size, sq_size, 1, 0, new_res, new_res, pz, 0), orientation='RPI')
# copy input qform matrix to reference image
im_ref.hdr.set_qform(im_input.hdr.get_qform())
im_ref.hdr.set_sform(im_input.hdr.get_sform())
# set correct header to reference image
im_ref.hdr.set_data_shape((sq_size, sq_size, 1))
im_ref.hdr.set_zooms((new_res, new_res, pz))
# save image to set orientation to RPI (not properly done at the creation of the image)
fname_ref = 'im_ref.nii.gz'
im_ref.setFileName(fname_ref)
im_ref.save()
im_ref = set_orientation(im_ref, 'RPI', fname_out=fname_ref)
# set header origin to zero to get physical coordinates of the center of the square
im_ref.hdr.as_analyze_map()['qoffset_x'] = 0
im_ref.hdr.as_analyze_map()['qoffset_y'] = 0
im_ref.hdr.as_analyze_map()['qoffset_z'] = 0
im_ref.hdr.set_sform(im_ref.hdr.get_qform())
im_ref.hdr.set_qform(im_ref.hdr.get_qform())
[[x_square_center_phys, y_square_center_phys, z_square_center_phys]] = im_ref.transfo_pix2phys(coordi=[[int(sq_size / 2), int(sq_size / 2), 0]])
list_interpolate_images = []
# iterate on z dimension of input image
for iz in range(nz):
# copy reference image: one reference image per slice
im_ref_slice_iz = im_ref.copy()
# get center of mass of SC for slice iz
x_seg, y_seg = (im_input_sc.data[:, :, iz] > 0).nonzero()
x_center, y_center = np.mean(x_seg), np.mean(y_seg)
[[x_center_phys, y_center_phys, z_center_phys]] = im_input_sc.transfo_pix2phys(coordi=[[x_center, y_center, iz]])
# center reference image on SC for slice iz
im_ref_slice_iz.hdr.as_analyze_map()['qoffset_x'] = x_center_phys - x_square_center_phys
im_ref_slice_iz.hdr.as_analyze_map()['qoffset_y'] = y_center_phys - y_square_center_phys
im_ref_slice_iz.hdr.as_analyze_map()['qoffset_z'] = z_center_phys
im_ref_slice_iz.hdr.set_sform(im_ref_slice_iz.hdr.get_qform())
im_ref_slice_iz.hdr.set_qform(im_ref_slice_iz.hdr.get_qform())
# interpolate input image to reference image
im_input_interpolate_iz = im_input.interpolate_from_image(im_ref_slice_iz, interpolation_mode=interpolation_mode, border='nearest')
# reshape data to 2D if needed
if len(im_input_interpolate_iz.data.shape) == 3:
im_input_interpolate_iz.data = im_input_interpolate_iz.data.reshape(im_input_interpolate_iz.data.shape[:-1])
# add slice to list
list_interpolate_images.append(im_input_interpolate_iz)
return list_interpolate_images
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 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'
class MultiLabelRegistration:
def __init__(self,
fname_gm,
fname_wm,
path_template,
fname_warp_template2target,
param=None,
fname_warp_target2template=None,
apply_warp_template=0,
fname_template_dest=None):
if param is None:
self.param = Param()
else:
self.param = param
self.im_gm = Image(fname_gm)
self.im_wm = Image(fname_wm)
self.path_template = sct.slash_at_the_end(path_template, 1)
# get GM and WM files from template:
fname_template_gm, fname_template_wm = None, None
for fname in os.listdir(self.path_template + 'template/'):
if 'gm' in fname.lower():
fname_template_gm = self.path_template + 'template/' + fname
elif 'wm' in fname.lower():
fname_template_wm = self.path_template + 'template/' + fname
if fname_template_gm is not None and fname_template_wm is not None:
self.im_template_gm = Image(fname_template_gm)
self.im_template_wm = Image(fname_template_wm)
if fname_template_gm.split('/')[-1] == 'MNI-Poly-AMU_GM.nii.gz':
self.template = 'MNI-Poly-AMU'
elif fname_template_gm.split('/')[-1] == 'PAM50_gm.nii.gz':
self.template = 'PAM50'
else:
self.template = 'custom'
# template file in its original space:
self.fname_template_dest = fname_template_dest
# Previous warping fields:
self.fname_warp_template2target = fname_warp_template2target
self.fname_warp_target2template = fname_warp_target2template
# new warping fields:
self.fname_warp_template2gm = ''
self.fname_warp_gm2template = ''
# temporary fix - related to issue #871
self.apply_warp_template = apply_warp_template
def register(self):
# accentuate separation WM/GM
self.im_gm = thr_im(self.im_gm, 0.01, self.param.thr)
self.im_wm = thr_im(self.im_wm, 0.01, self.param.thr)
self.im_template_gm = thr_im(self.im_template_gm, 0.01, self.param.thr)
self.im_template_wm = thr_im(self.im_template_wm, 0.01, self.param.thr)
# create multilabel images:
# copy GM images to keep header information
im_automatic_ml = self.im_gm.copy()
im_template_ml = self.im_template_gm.copy()
# create multi-label segmentation with GM*200 + WM*100 (100 and 200 encoded in self.param.gap)
im_automatic_ml.data = self.param.gap[
1] * self.im_gm.data + self.param.gap[0] * self.im_wm.data
im_template_ml.data = self.param.gap[
1] * self.im_template_gm.data + self.param.gap[
0] * self.im_template_wm.data
# set new names
fname_automatic_ml = 'multilabel_automatic_seg.nii.gz'
fname_template_ml = 'multilabel_template_seg.nii.gz'
im_automatic_ml.setFileName(fname_automatic_ml)
im_template_ml.setFileName(fname_template_ml)
# Create temporary folder and put files in it
tmp_dir = sct.tmp_create()
path_gm, file_gm, ext_gm = sct.extract_fname(fname_gm)
path_warp_template2target, file_warp_template2target, ext_warp_template2target = sct.extract_fname(
self.fname_warp_template2target)
convert(fname_gm, tmp_dir + file_gm + ext_gm)
convert(fname_warp_template,
tmp_dir + file_warp_template2target + ext_warp_template2target,
squeeze_data=0)
if self.fname_warp_target2template is not None:
path_warp_target2template, file_warp_target2template, ext_warp_target2template = sct.extract_fname(
self.fname_warp_target2template)
convert(self.fname_warp_target2template,
tmp_dir + file_warp_target2template +
ext_warp_target2template,
squeeze_data=0)
os.chdir(tmp_dir)
# save images
im_automatic_ml.save()
im_template_ml.save()
# apply template2image warping field
if self.apply_warp_template == 1:
fname_template_ml_new = sct.add_suffix(fname_template_ml, '_r')
sct.run('sct_apply_transfo -i ' + fname_template_ml + ' -d ' +
fname_automatic_ml + ' -w ' + file_warp_template2target +
ext_warp_template2target + ' -o ' + fname_template_ml_new)
fname_template_ml = fname_template_ml_new
nx, ny, nz, nt, px, py, pz, pt = im_automatic_ml.dim
size_mask = int(22.5 / px)
fname_mask = 'square_mask.nii.gz'
sct.run('sct_create_mask -i ' + fname_automatic_ml +
' -p centerline,' + fname_automatic_ml + ' -f box -size ' +
str(size_mask) + ' -o ' + fname_mask)
fname_automatic_ml, xi, xf, yi, yf, zi, zf = crop_im(
fname_automatic_ml, fname_mask)
fname_template_ml, xi, xf, yi, yf, zi, zf = crop_im(
fname_template_ml, fname_mask)
# fname_automatic_ml_smooth = sct.add_suffix(fname_automatic_ml, '_smooth')
# sct.run('sct_maths -i '+fname_automatic_ml+' -smooth '+str(self.param.smooth)+','+str(self.param.smooth)+',0 -o '+fname_automatic_ml_smooth)
# fname_automatic_ml = fname_automatic_ml_smooth
path_automatic_ml, file_automatic_ml, ext_automatic_ml = sct.extract_fname(
fname_automatic_ml)
path_template_ml, file_template_ml, ext_template_ml = sct.extract_fname(
fname_template_ml)
# Register multilabel images together
cmd_reg = 'sct_register_multimodal -i ' + fname_template_ml + ' -d ' + fname_automatic_ml + ' -param ' + self.param.param_reg
if 'centermass' in self.param.param_reg:
fname_template_ml_seg = sct.add_suffix(fname_template_ml, '_bin')
sct.run('sct_maths -i ' + fname_template_ml + ' -bin 0 -o ' +
fname_template_ml_seg)
fname_automatic_ml_seg = sct.add_suffix(fname_automatic_ml, '_bin')
# sct.run('sct_maths -i '+fname_automatic_ml+' -thr 50 -o '+fname_automatic_ml_seg)
sct.run('sct_maths -i ' + fname_automatic_ml + ' -bin 50 -o ' +
fname_automatic_ml_seg)
cmd_reg += ' -iseg ' + fname_template_ml_seg + ' -dseg ' + fname_automatic_ml_seg
sct.run(cmd_reg)
fname_warp_multilabel_template2auto = 'warp_' + file_template_ml + '2' + file_automatic_ml + '.nii.gz'
fname_warp_multilabel_auto2template = 'warp_' + file_automatic_ml + '2' + file_template_ml + '.nii.gz'
self.fname_warp_template2gm = sct.extract_fname(
self.fname_warp_template2target
)[1] + '_reg_gm' + sct.extract_fname(
self.fname_warp_template2target)[2]
# fname_warp_multilabel_template2auto = pad_im(fname_warp_multilabel_template2auto, nx, ny, nz, xi, xf, yi, yf, zi, zf)
# fname_warp_multilabel_auto2template = pad_im(fname_warp_multilabel_auto2template, nx, ny, nz, xi, xf, yi, yf, zi, zf)
sct.run('sct_concat_transfo -w ' + file_warp_template2target +
ext_warp_template2target + ',' +
fname_warp_multilabel_template2auto + ' -d ' + file_gm +
ext_gm + ' -o ' + self.fname_warp_template2gm)
if self.fname_warp_target2template is not None:
if self.fname_template_dest is None:
path_script = os.path.dirname(__file__)
path_sct = os.path.dirname(path_script)
if self.template == 'MNI-Poly-AMU':
self.fname_template_dest = path_sct + '/data/MNI-Poly-AMU/template/MNI-Poly-AMU_T2.nii.gz'
elif self.template == 'PAM50':
self.fname_template_dest = path_sct + '/data/PAM50/template/PAM50_t2.nii.gz'
self.fname_warp_gm2template = sct.extract_fname(
self.fname_warp_target2template
)[1] + '_reg_gm' + sct.extract_fname(
self.fname_warp_target2template)[2]
sct.run('sct_concat_transfo -w ' +
fname_warp_multilabel_auto2template + ',' +
file_warp_target2template + ext_warp_target2template +
' -d ' + self.fname_template_dest + ' -o ' +
self.fname_warp_gm2template)
os.chdir('..')
# sct.generate_output_file(tmp_dir+fname_warp_multilabel_template2auto, self.param.output_folder+'warp_template_multilabel2automatic_seg_multilabel.nii.gz')
# sct.generate_output_file(tmp_dir+fname_warp_multilabel_auto2template, self.param.output_folder+'warp_automatic_seg_multilabel2template_multilabel.nii.gz')
sct.generate_output_file(
tmp_dir + self.fname_warp_template2gm,
self.param.output_folder + self.fname_warp_template2gm)
if self.fname_warp_target2template is not None:
sct.generate_output_file(
tmp_dir + self.fname_warp_gm2template,
self.param.output_folder + self.fname_warp_gm2template)
if self.param.qc:
fname_grid_warped = visualize_warp(
tmp_dir + fname_warp_multilabel_template2auto,
rm_tmp=self.param.remove_tmp)
path_grid_warped, file_grid_warped, ext_grid_warped = sct.extract_fname(
fname_grid_warped)
sct.generate_output_file(
fname_grid_warped,
self.param.output_folder + file_grid_warped + ext_grid_warped)
if self.param.remove_tmp:
sct.run('rm -rf ' + tmp_dir, error_exit='warning')
def validation(self, fname_manual_gmseg, fname_sc_seg):
path_manual_gmseg, file_manual_gmseg, ext_manual_gmseg = sct.extract_fname(
fname_manual_gmseg)
path_sc_seg, file_sc_seg, ext_sc_seg = sct.extract_fname(fname_sc_seg)
# Create tmp folder and copy files in it
tmp_dir = sct.tmp_create()
sct.run('cp ' + fname_manual_gmseg + ' ' + tmp_dir +
file_manual_gmseg + ext_manual_gmseg)
sct.run('cp ' + fname_sc_seg + ' ' + tmp_dir + file_sc_seg +
ext_sc_seg)
sct.run('cp ' + self.param.output_folder +
self.fname_warp_template2gm + ' ' + tmp_dir +
self.fname_warp_template2gm)
os.chdir(tmp_dir)
sct.run('sct_warp_template -d ' + fname_manual_gmseg + ' -w ' +
self.fname_warp_template2gm + ' -qc 0 -a 0')
if 'MNI-Poly-AMU_GM.nii.gz' in os.listdir('label/template/'):
im_new_template_gm = Image('label/template/MNI-Poly-AMU_GM.nii.gz')
im_new_template_wm = Image('label/template/MNI-Poly-AMU_WM.nii.gz')
else:
im_new_template_gm = Image('label/template/PAM50_gm.nii.gz')
im_new_template_wm = Image('label/template/PAM50_wm.nii.gz')
im_new_template_gm = thr_im(im_new_template_gm, self.param.thr,
self.param.thr)
im_new_template_wm = thr_im(im_new_template_wm, self.param.thr,
self.param.thr)
self.im_template_gm = thr_im(self.im_template_gm, self.param.thr,
self.param.thr)
self.im_template_wm = thr_im(self.im_template_wm, self.param.thr,
self.param.thr)
fname_new_template_gm = 'new_template_gm.nii.gz'
im_new_template_gm.setFileName(fname_new_template_gm)
im_new_template_gm.save()
fname_new_template_wm = 'new_template_wm.nii.gz'
im_new_template_wm.setFileName(fname_new_template_wm)
im_new_template_wm.save()
fname_old_template_wm = 'old_template_wm.nii.gz'
self.im_template_wm.setFileName(fname_old_template_wm)
self.im_template_wm.save()
fname_old_template_gm = 'old_template_gm.nii.gz'
self.im_template_gm.setFileName(fname_old_template_gm)
self.im_template_gm.save()
fname_manual_wmseg = 'target_manual_wmseg.nii.gz'
sct.run('sct_maths -i ' + file_sc_seg + ext_sc_seg + ' -sub ' +
file_manual_gmseg + ext_manual_gmseg + ' -o ' +
fname_manual_wmseg)
# Compute Hausdorff distance
status, output_old_hd = sct.run('sct_compute_hausdorff_distance -i ' +
fname_old_template_gm + ' -r ' +
file_manual_gmseg + ext_manual_gmseg +
' -t 1 -v 1')
status, output_new_hd = sct.run('sct_compute_hausdorff_distance -i ' +
fname_new_template_gm + ' -r ' +
file_manual_gmseg + ext_manual_gmseg +
' -t 1 -v 1')
hd_name = 'hd_md_multilabel_reg.txt'
hd_fic = open(hd_name, 'w')
hd_fic.write(
'The "diff" columns are comparisons between regular template registration and corrected template registration according to SC internal structure\n'
'Diff = metric_regular_reg - metric_corrected_reg\n')
hd_fic.write('#Slice, HD, HD diff, MD, MD diff\n')
no_ref_slices = []
init_hd = "Hausdorff's distance - First relative Hausdorff's distance median - Second relative Hausdorff's distance median(all in mm)\n"
old_gm_hd = output_old_hd[output_old_hd.find(init_hd) +
len(init_hd):].split('\n')
new_gm_hd = output_new_hd[output_new_hd.find(init_hd) +
len(init_hd):].split('\n')
for i in range(len(old_gm_hd) - 3): # last two lines are informations
i_old, val_old = old_gm_hd[i].split(':')
i_new, val_new = new_gm_hd[i].split(':')
i_old = int(i_old[-2:])
i_new = int(i_new[-2:])
assert i == i_old == i_new, 'ERROR: when comparing Hausdorff distances, slice numbers differs.'
hd_old, med1_old, med2_old = val_old.split('-')
hd_new, med1_new, med2_new = val_new.split('-')
if float(hd_old) == 0.0:
no_ref_slices.append(i)
hd_fic.write(str(i) + ', NO MANUAL SEGMENTATION\n')
else:
md_new = max(float(med1_new), float(med2_new))
md_old = max(float(med1_old), float(med2_old))
hd_fic.write(
str(i) + ', ' + hd_new + ', ' +
str(float(hd_old) - float(hd_new)) + ', ' + str(md_new) +
', ' + str(md_old - md_new) + '\n')
hd_fic.close()
# Compute Dice coefficient
# --- DC old template
try:
status_old_gm, output_old_gm = sct.run(
'sct_dice_coefficient -i ' + file_manual_gmseg +
ext_manual_gmseg + ' -d ' + fname_old_template_gm +
' -2d-slices 2',
error_exit='warning',
raise_exception=True)
except Exception:
# put the result and the reference in the same space using a registration with ANTs with no iteration:
corrected_manual_gmseg = file_manual_gmseg + '_in_old_template_space' + ext_manual_gmseg
sct.run('isct_antsRegistration -d 3 -t Translation[0] -m MI[' +
fname_old_template_gm + ',' + file_manual_gmseg +
ext_manual_gmseg + ',1,16] -o [reg_ref_to_res,' +
corrected_manual_gmseg + '] -n BSpline[3] -c 0 -f 1 -s 0')
# sct.run('sct_maths -i '+corrected_manual_gmseg+' -thr 0.1 -o '+corrected_manual_gmseg)
sct.run('sct_maths -i ' + corrected_manual_gmseg +
' -bin 0.1 -o ' + corrected_manual_gmseg)
status_old_gm, output_old_gm = sct.run(
'sct_dice_coefficient -i ' + corrected_manual_gmseg + ' -d ' +
fname_old_template_gm + ' -2d-slices 2',
error_exit='warning')
try:
status_old_wm, output_old_wm = sct.run(
'sct_dice_coefficient -i ' + fname_manual_wmseg + ' -d ' +
fname_old_template_wm + ' -2d-slices 2',
error_exit='warning',
raise_exception=True)
except Exception:
# put the result and the reference in the same space using a registration with ANTs with no iteration:
path_manual_wmseg, file_manual_wmseg, ext_manual_wmseg = sct.extract_fname(
fname_manual_wmseg)
corrected_manual_wmseg = file_manual_wmseg + '_in_old_template_space' + ext_manual_wmseg
sct.run('isct_antsRegistration -d 3 -t Translation[0] -m MI[' +
fname_old_template_wm + ',' + fname_manual_wmseg +
',1,16] -o [reg_ref_to_res,' + corrected_manual_wmseg +
'] -n BSpline[3] -c 0 -f 1 -s 0')
# sct.run('sct_maths -i '+corrected_manual_wmseg+' -thr 0.1 -o '+corrected_manual_wmseg)
sct.run('sct_maths -i ' + corrected_manual_wmseg +
' -bin 0.1 -o ' + corrected_manual_wmseg)
status_old_wm, output_old_wm = sct.run(
'sct_dice_coefficient -i ' + corrected_manual_wmseg + ' -d ' +
fname_old_template_wm + ' -2d-slices 2',
error_exit='warning')
# --- DC new template
try:
status_new_gm, output_new_gm = sct.run(
'sct_dice_coefficient -i ' + file_manual_gmseg +
ext_manual_gmseg + ' -d ' + fname_new_template_gm +
' -2d-slices 2',
error_exit='warning',
raise_exception=True)
except Exception:
# put the result and the reference in the same space using a registration with ANTs with no iteration:
corrected_manual_gmseg = file_manual_gmseg + '_in_new_template_space' + ext_manual_gmseg
sct.run('isct_antsRegistration -d 3 -t Translation[0] -m MI[' +
fname_new_template_gm + ',' + file_manual_gmseg +
ext_manual_gmseg + ',1,16] -o [reg_ref_to_res,' +
corrected_manual_gmseg + '] -n BSpline[3] -c 0 -f 1 -s 0')
# sct.run('sct_maths -i '+corrected_manual_gmseg+' -thr 0.1 -o '+corrected_manual_gmseg)
sct.run('sct_maths -i ' + corrected_manual_gmseg +
' -bin 0.1 -o ' + corrected_manual_gmseg)
status_new_gm, output_new_gm = sct.run(
'sct_dice_coefficient -i ' + corrected_manual_gmseg + ' -d ' +
fname_new_template_gm + ' -2d-slices 2',
error_exit='warning')
try:
status_new_wm, output_new_wm = sct.run(
'sct_dice_coefficient -i ' + fname_manual_wmseg + ' -d ' +
fname_new_template_wm + ' -2d-slices 2',
error_exit='warning',
raise_exception=True)
except Exception:
# put the result and the reference in the same space using a registration with ANTs with no iteration:
path_manual_wmseg, file_manual_wmseg, ext_manual_wmseg = sct.extract_fname(
fname_manual_wmseg)
corrected_manual_wmseg = file_manual_wmseg + '_in_new_template_space' + ext_manual_wmseg
sct.run('isct_antsRegistration -d 3 -t Translation[0] -m MI[' +
fname_new_template_wm + ',' + fname_manual_wmseg +
',1,16] -o [reg_ref_to_res,' + corrected_manual_wmseg +
'] -n BSpline[3] -c 0 -f 1 -s 0')
# sct.run('sct_maths -i '+corrected_manual_wmseg+' -thr 0.1 -o '+corrected_manual_wmseg)
sct.run('sct_maths -i ' + corrected_manual_wmseg +
' -bin 0.1 -o ' + corrected_manual_wmseg)
status_new_wm, output_new_wm = sct.run(
'sct_dice_coefficient -i ' + corrected_manual_wmseg + ' -d ' +
fname_new_template_wm + ' -2d-slices 2',
error_exit='warning')
dice_name = 'dice_multilabel_reg.txt'
dice_fic = open(dice_name, 'w')
dice_fic.write(
'The "diff" columns are comparisons between regular template registration and corrected template registration according to SC internal structure\n'
'Diff = metric_corrected_reg - metric_regular_reg\n')
dice_fic.write('#Slice, WM DC, WM diff, GM DC, GM diff\n')
init_dc = '2D Dice coefficient by slice:\n'
old_gm_dc = output_old_gm[output_old_gm.find(init_dc) +
len(init_dc):].split('\n')
old_wm_dc = output_old_wm[output_old_wm.find(init_dc) +
len(init_dc):].split('\n')
new_gm_dc = output_new_gm[output_new_gm.find(init_dc) +
len(init_dc):].split('\n')
new_wm_dc = output_new_wm[output_new_wm.find(init_dc) +
len(init_dc):].split('\n')
for i in range(len(old_gm_dc)):
if i not in no_ref_slices:
i_new_gm, val_new_gm = new_gm_dc[i].split(' ')
i_new_wm, val_new_wm = new_wm_dc[i].split(' ')
i_old_gm, val_old_gm = old_gm_dc[i].split(' ')
i_old_wm, val_old_wm = old_wm_dc[i].split(' ')
assert i == int(i_new_gm) == int(i_new_wm) == int(
i_old_gm
) == int(
i_old_wm
), 'ERROR: when comparing Dice coefficients, slice numbers differs.'
dice_fic.write(
str(i) + ', ' + val_new_wm + ', ' +
str(float(val_new_wm) - float(val_old_wm)) + ', ' +
val_new_gm + ', ' +
str(float(val_new_gm) - float(val_old_gm)) + '\n')
else:
dice_fic.write(str(i) + ', NO MANUAL SEGMENTATION\n')
dice_fic.close()
os.chdir('..')
sct.generate_output_file(tmp_dir + hd_name,
self.param.output_folder + hd_name)
sct.generate_output_file(tmp_dir + dice_name,
self.param.output_folder + dice_name)
if self.param.remove_tmp:
sct.run('rm -rf ' + tmp_dir, error_exit='warning')
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)
zsum = np.sum(np.sum(data_cord, 0), 0)
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