def compute_ratio(self):
type_ratio = self.param_seg.ratio
tmp_dir_ratio = 'tmp_ratio/'
os.mkdir(tmp_dir_ratio)
os.chdir(tmp_dir_ratio)
fname_gmseg = self.im_res_gmseg.absolutepath
fname_wmseg = self.im_res_wmseg.absolutepath
self.im_res_gmseg.save()
self.im_res_wmseg.save()
if self.im_res_gmseg.orientation is not 'RPI':
im_res_gmseg = set_orientation(self.im_res_gmseg, 'RPI')
im_res_wmseg = set_orientation(self.im_res_wmseg, 'RPI')
fname_gmseg = im_res_gmseg.absolutepath
fname_wmseg = im_res_wmseg.absolutepath
sct_process_segmentation.main(['-i', fname_gmseg, '-p', 'csa', '-ofolder', 'gm_csa', '-no-angle', '1'])
sct_process_segmentation.main(['-i', fname_wmseg, '-p', 'csa', '-ofolder', 'wm_csa', '-no-angle', '1'])
gm_csa = open('gm_csa/csa_per_slice.txt', 'r')
wm_csa = open('wm_csa/csa_per_slice.txt', 'r')
gm_csa_lines = gm_csa.readlines()
wm_csa_lines = wm_csa.readlines()
gm_csa.close()
wm_csa.close()
fname_ratio = 'ratio_by_' + type_ratio + '.txt'
file_ratio = open(fname_ratio, 'w')
file_ratio.write(type_ratio + ', ratio GM/WM CSA\n')
csa_gm_wm_by_level = {0: [], 1: [], 2: [], 3: [], 4: [], 5: [], 6: [], 7: [], 8: [], 9: [], 10: [], 11: [], 12: [], 13: [], 14: [], 15: [], 16: [], 17: [], 18: [], 19: [], 20: [], 21: [], 22: [], 23: [], 24: []}
for gm_line, wm_line in zip(gm_csa_lines[1:], wm_csa_lines[1:]):
i, gm_area, gm_angle = gm_line.split(',')
j, wm_area, wm_angle = wm_line.split(',')
assert i == j
if type_ratio == 'level':
level_slice = int(self.target_im[int(i)].level)
csa_gm_wm_by_level[level_slice].append((float(gm_area), float(wm_area)))
else:
file_ratio.write(i + ', ' + str(float(gm_area) / float(wm_area)) + '\n')
if type_ratio == 'level':
for l, gm_wm_list in sorted(csa_gm_wm_by_level.items()):
if str(gm_wm_list) != '[]':
csa_gm_list = []
csa_wm_list = []
for gm, wm in gm_wm_list:
csa_gm_list.append(gm)
csa_wm_list.append(wm)
csa_gm = np.mean(csa_gm_list)
csa_wm = np.mean(csa_wm_list)
file_ratio.write(str(l) + ', ' + str(csa_gm / csa_wm) + '\n')
file_ratio.close()
shutil.copy(fname_ratio, '../../' + self.param_seg.path_results + '/' + fname_ratio)
os.chdir('..')
def extract_slices(self):
# open image and re-orient it to RPI if needed
im, seg = Image(self.param.fname_im), Image(self.param.fname_seg)
if self.orientation_im != self.orientation_extraction:
im, seg = set_orientation(im, self.orientation_extraction), set_orientation(seg, self.orientation_extraction)
# extract axial slices in self.dct_im_seg
self.dct_im_seg['im'], self.dct_im_seg['seg'] = [im.data[:, :, z] for z in range(im.dim[2])], [seg.data[:, :, z] for z in range(im.dim[2])]
def resample_image(fname,
suffix='_resampled.nii.gz',
binary=False,
npx=0.3,
npy=0.3,
thr=0.0,
interpolation='spline'):
"""
Resampling function: add a padding, resample, crop the padding
:param fname: name of the image file to be resampled
:param suffix: suffix added to the original fname after resampling
:param binary: boolean, image is binary or not
:param npx: new pixel size in the x direction
:param npy: new pixel size in the y direction
:param thr: if the image is binary, it will be thresholded at thr (default=0) after the resampling
:param interpolation: type of interpolation used for the resampling
:return: file name after resampling (or original fname if it was already in the correct resolution)
"""
im_in = Image(fname)
orientation = get_orientation_3d(im_in)
if orientation != 'RPI':
im_in = set_orientation(im_in, 'RPI')
im_in.save()
fname = im_in.absolutepath
nx, ny, nz, nt, px, py, pz, pt = im_in.dim
if round(px, 2) != round(npx, 2) or round(py, 2) != round(npy, 2):
name_resample = sct.extract_fname(fname)[1] + suffix
if binary:
interpolation = 'nn'
sct.run('sct_resample -i ' + fname + ' -mm ' + str(npx) + 'x' +
str(npy) + 'x' + str(pz) + ' -o ' + name_resample + ' -x ' +
interpolation)
if binary:
# sct.run('sct_maths -i ' + name_resample + ' -thr ' + str(thr) + ' -o ' + name_resample)
sct.run('sct_maths -i ' + name_resample + ' -bin ' + str(thr) +
' -o ' + name_resample)
if orientation != 'RPI':
im_resample = Image(name_resample)
im_resample = set_orientation(im_resample, orientation)
im_resample.save()
name_resample = im_resample.absolutepath
return name_resample
else:
if orientation != 'RPI':
im_in = set_orientation(im_in, orientation)
im_in.save()
fname = im_in.absolutepath
sct.printv('Image resolution already ' + str(npx) + 'x' + str(npy) +
'xpz')
return fname
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 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 resample_image(fname, suffix='_resampled.nii.gz', binary=False, npx=0.3, npy=0.3, thr=0.0, interpolation='spline'):
"""
Resampling function: add a padding, resample, crop the padding
:param fname: name of the image file to be resampled
:param suffix: suffix added to the original fname after resampling
:param binary: boolean, image is binary or not
:param npx: new pixel size in the x direction
:param npy: new pixel size in the y direction
:param thr: if the image is binary, it will be thresholded at thr (default=0) after the resampling
:param interpolation: type of interpolation used for the resampling
:return: file name after resampling (or original fname if it was already in the correct resolution)
"""
im_in = Image(fname)
orientation = get_orientation(im_in)
if orientation != 'RPI':
im_in = set_orientation(im_in, 'RPI')
im_in.save()
fname = im_in.absolutepath
nx, ny, nz, nt, px, py, pz, pt = im_in.dim
if round(px, 2) != round(npx, 2) or round(py, 2) != round(npy, 2):
name_resample = sct.extract_fname(fname)[1] + suffix
if binary:
interpolation = 'nn'
if nz == 1: # when data is 2d: we convert it to a 3d image in order to avoid nipy problem of conversion nifti-->nipy with 2d data
sct.run(['sct_image', '-i', ','.join([fname, fname]), '-concat', 'z', '-o', fname])
sct.run(['sct_resample', '-i', fname, '-mm', str(npx) + 'x' + str(npy) + 'x' + str(pz), '-o', name_resample, '-x', interpolation])
if nz == 1: # when input data was 2d: re-convert data 3d-->2d
sct.run(['sct_image', '-i', name_resample, '-split', 'z'])
im_split = Image(name_resample.split('.nii.gz')[0] + '_Z0000.nii.gz')
im_split.setFileName(name_resample)
im_split.save()
if binary:
sct.run(['sct_maths', '-i', name_resample, '-bin', str(thr), '-o', name_resample])
if orientation != 'RPI':
im_resample = Image(name_resample)
im_resample = set_orientation(im_resample, orientation)
im_resample.save()
name_resample = im_resample.absolutepath
return name_resample
else:
if orientation != 'RPI':
im_in = set_orientation(im_in, orientation)
im_in.save()
fname = im_in.absolutepath
sct.printv('Image resolution already ' + str(npx) + 'x' + str(npy) + 'xpz')
return fname
def orient2pir(self):
"""Orient input data to PIR orientation."""
if self.orientation_im != 'PIR': # open image and re-orient it to PIR if needed
im_tmp = Image(self.fname_im)
set_orientation(im_tmp,
'PIR',
fname_out=''.join(
sct.extract_fname(self.fname_im)[1:]))
if self.fname_seg is not None:
set_orientation(Image(self.fname_seg),
'PIR',
fname_out=''.join(
sct.extract_fname(self.fname_seg)[1:]))
def post_processing(self):
square_mask = Image(self.preprocessed.square_mask)
tmp_res_names = []
for res_im in [self.gm_seg.res_wm_seg, self.gm_seg.res_gm_seg, self.gm_seg.corrected_wm_seg]:
res_im_original_space = inverse_square_crop(res_im, square_mask)
res_im_original_space.save()
res_im_original_space = set_orientation(res_im_original_space, self.preprocessed.original_orientation)
res_im_original_space.save()
res_fname_original_space = res_im_original_space.file_name
ext = res_im_original_space.ext
# crop from the same pad size
output_crop = res_fname_original_space+'_crop'
sct.run('sct_crop_image -i '+res_fname_original_space+ext+' -dim 0,1,2 -start '+self.preprocessed.pad[0]+','+self.preprocessed.pad[1]+','+self.preprocessed.pad[2]+' -end -'+self.preprocessed.pad[0]+',-'+self.preprocessed.pad[1]+',-'+self.preprocessed.pad[2]+' -o '+output_crop+ext)
res_fname_original_space = output_crop
target_path, target_name, target_ext = sct.extract_fname(self.target_fname)
res_name = target_name + res_im.file_name[len(sct.extract_fname(self.preprocessed.processed_target)[1]):] + '.nii.gz'
if self.seg_param.res_type == 'binary':
bin = True
else:
bin = False
old_res_name = resample_image(res_fname_original_space+ext, npx=self.preprocessed.original_px, npy=self.preprocessed.original_py, binary=bin)
if self.seg_param.res_type == 'prob':
# sct.run('fslmaths ' + old_res_name + ' -thr 0.05 ' + old_res_name)
sct.run('sct_maths -i ' + old_res_name + ' -thr 0.05 -o ' + old_res_name)
sct.run('cp ' + old_res_name + ' '+res_name)
tmp_res_names.append(res_name)
self.res_names['wm_seg'] = tmp_res_names[0]
self.res_names['gm_seg'] = tmp_res_names[1]
self.res_names['corrected_wm_seg'] = tmp_res_names[2]
def __init__(self, im, v=1):
sct.printv('Thinning ... ', v, 'normal')
self.image = im
self.image.data = bin_data(self.image.data)
self.dim_im = len(self.image.data.shape)
if self.dim_im == 2:
self.thinned_image = Image(param=self.zhang_suen(self.image.data),
absolutepath=self.image.path +
self.image.file_name + '_thinned' +
self.image.ext,
hdr=self.image.hdr)
elif self.dim_im == 3:
if not self.image.orientation == 'IRP':
from sct_image import set_orientation
print '-- changing orientation ...'
self.image = set_orientation(self.image, 'IRP')
thinned_data = np.asarray(
[self.zhang_suen(im_slice) for im_slice in self.image.data])
self.thinned_image = Image(param=thinned_data,
absolutepath=self.image.path +
self.image.file_name + '_thinned' +
self.image.ext,
hdr=self.image.hdr)
def load_manual_gmseg(list_slices_target,
list_fname_manual_gmseg,
tmp_dir,
im_sc_seg_rpi,
new_res,
square_size_size_mm,
for_model=False,
fname_mask=None):
if isinstance(list_fname_manual_gmseg, str):
# consider fname_manual_gmseg as a list of file names to allow multiple manual GM segmentation
list_fname_manual_gmseg = [list_fname_manual_gmseg]
for fname_manual_gmseg in list_fname_manual_gmseg:
os.chdir('..')
shutil.copy(fname_manual_gmseg, tmp_dir)
# change fname level to only file name (path = tmp dir now)
path_gm, file_gm, ext_gm = extract_fname(fname_manual_gmseg)
fname_manual_gmseg = file_gm + ext_gm
os.chdir(tmp_dir)
im_manual_gmseg = Image(fname_manual_gmseg)
# reorient to RPI
im_manual_gmseg = set_orientation(im_manual_gmseg, 'RPI')
if fname_mask is not None:
fname_gmseg_crop = add_suffix(im_manual_gmseg.absolutepath,
'_pre_crop')
crop_im = ImageCropper(input_file=im_manual_gmseg.absolutepath,
output_file=fname_gmseg_crop,
mask=fname_mask)
im_manual_gmseg_crop = crop_im.crop()
im_manual_gmseg = im_manual_gmseg_crop
# assert gmseg has the right number of slices
assert im_manual_gmseg.data.shape[2] == len(
list_slices_target
), 'ERROR: the manual GM segmentation has not the same number of slices than the image.'
# interpolate gm to reference image
nz_gmseg, nx_gmseg, ny_gmseg, nt_gmseg, pz_gmseg, px_gmseg, py_gmseg, pt_gmseg = im_manual_gmseg.dim
list_im_gm = interpolate_im_to_ref(im_manual_gmseg,
im_sc_seg_rpi,
new_res=new_res,
sq_size_size_mm=square_size_size_mm,
interpolation_mode=0)
# load gm seg in list of slices
n_poped = 0
for im_gm, slice_im in zip(list_im_gm, list_slices_target):
if im_gm.data.max() == 0 and for_model:
list_slices_target.pop(slice_im.id - n_poped)
n_poped += 1
else:
slice_im.gm_seg.append(im_gm.data)
wm_slice = (slice_im.im > 0) - im_gm.data
slice_im.wm_seg.append(wm_slice)
return list_slices_target
def generate_mask_pmj(self):
"""Output the PMJ mask."""
if self.pa_coord != -1: # If PMJ has been detected
im = Image(''.join(extract_fname(
self.fname_im)[1:])) # image in PIR orientation
im_mask = im.copy()
im_mask.data *= 0 # empty mask
im_mask.data[self.pa_coord, self.is_coord,
self.rl_coord] = 50 # voxel with value = 50
if self.quality_control: # output QC image
self.save_qc(im.data[:, :, self.rl_coord],
[self.pa_coord, self.is_coord])
im_mask.setFileName(self.fname_out)
im_mask = set_orientation(
im_mask, self.orientation_im,
fname_out=self.fname_out) # reorient data
x_pmj, y_pmj, z_pmj = np.where(im_mask.data == 50)
printv('\tx_pmj = ' + str(x_pmj[0]), self.verbose, 'info')
printv('\ty_pmj = ' + str(y_pmj[0]), self.verbose, 'info')
printv('\tz_pmj = ' + str(z_pmj[0]), self.verbose, 'info')
im_mask.save()
def reorient_data(self):
for f in self.fname_metric_lst:
os.rename(self.fname_metric_lst[f], add_suffix(''.join(extract_fname(self.param.fname_im)[1:]), '_2reorient'))
im = Image(add_suffix(''.join(extract_fname(self.param.fname_im)[1:]), '_2reorient'))
im = set_orientation(im, self.orientation_im)
im.setFileName(self.fname_metric_lst[f])
im.save()
def resample_image(fname, suffix='_resampled.nii.gz', binary=False, npx=0.3, npy=0.3, thr=0.0, interpolation='spline'):
"""
Resampling function: add a padding, resample, crop the padding
:param fname: name of the image file to be resampled
:param suffix: suffix added to the original fname after resampling
:param binary: boolean, image is binary or not
:param npx: new pixel size in the x direction
:param npy: new pixel size in the y direction
:param thr: if the image is binary, it will be thresholded at thr (default=0) after the resampling
:param interpolation: type of interpolation used for the resampling
:return: file name after resampling (or original fname if it was already in the correct resolution)
"""
im_in = Image(fname)
orientation = get_orientation_3d(im_in)
if orientation != 'RPI':
im_in = set_orientation(im_in, 'RPI')
im_in.save()
fname = im_in.absolutepath
nx, ny, nz, nt, px, py, pz, pt = im_in.dim
if round(px, 2) != round(npx, 2) or round(py, 2) != round(npy, 2):
name_resample = sct.extract_fname(fname)[1] + suffix
if binary:
interpolation = 'nn'
sct.run('sct_resample -i '+fname+' -mm '+str(npx)+'x'+str(npy)+'x'+str(pz)+' -o '+name_resample+' -x '+interpolation)
if binary:
# sct.run('sct_maths -i ' + name_resample + ' -thr ' + str(thr) + ' -o ' + name_resample)
sct.run('sct_maths -i ' + name_resample + ' -bin ' + str(thr) + ' -o ' + name_resample)
if orientation != 'RPI':
im_resample = Image(name_resample)
im_resample = set_orientation(im_resample, orientation)
im_resample.save()
name_resample = im_resample.absolutepath
return name_resample
else:
if orientation != 'RPI':
im_in = set_orientation(im_in, orientation)
im_in.save()
fname = im_in.absolutepath
sct.printv('Image resolution already ' + str(npx) + 'x' + str(npy) + 'xpz')
return fname
def compute_texture(self):
offset = int(self.param_glcm.distance)
printv('\nCompute texture metrics...', self.param.verbose, 'normal')
# open image and re-orient it to RPI if needed
im_tmp = Image(self.param.fname_im)
if self.orientation_im != self.orientation_extraction:
im_tmp = set_orientation(im_tmp, self.orientation_extraction)
dct_metric = {}
for m in self.metric_lst:
im_2save = im_tmp.copy()
im_2save.changeType(type='float64')
im_2save.data *= 0
dct_metric[m] = im_2save
# dct_metric[m] = Image(self.fname_metric_lst[m])
timer = Timer(number_of_iteration=len(self.dct_im_seg['im']))
timer.start()
for im_z, seg_z, zz in zip(self.dct_im_seg['im'], self.dct_im_seg['seg'], range(len(self.dct_im_seg['im']))):
for xx in range(im_z.shape[0]):
for yy in range(im_z.shape[1]):
if not seg_z[xx, yy]:
continue
if xx < offset or yy < offset:
continue
if xx > (im_z.shape[0] - offset - 1) or yy > (im_z.shape[1] - offset - 1):
continue # to check if the whole glcm_window is in the axial_slice
if False in np.unique(seg_z[xx - offset: xx + offset + 1, yy - offset: yy + offset + 1]):
continue # to check if the whole glcm_window is in the mask of the axial_slice
glcm_window = im_z[xx - offset: xx + offset + 1, yy - offset: yy + offset + 1]
glcm_window = glcm_window.astype(np.uint8)
dct_glcm = {}
for a in self.param_glcm.angle.split(','): # compute the GLCM for self.param_glcm.distance and for each self.param_glcm.angle
dct_glcm[a] = greycomatrix(glcm_window,
[self.param_glcm.distance], [radians(int(a))],
symmetric=self.param_glcm.symmetric,
normed=self.param_glcm.normed)
for m in self.metric_lst: # compute the GLCM property (m.split('_')[0]) of the voxel xx,yy,zz
dct_metric[m].data[xx, yy, zz] = greycoprops(dct_glcm[m.split('_')[2]], m.split('_')[0])[0][0]
timer.add_iteration()
timer.stop()
for m in self.metric_lst:
fname_out = add_suffix(''.join(extract_fname(self.param.fname_im)[1:]), '_' + m)
dct_metric[m].setFileName(fname_out)
dct_metric[m].save()
self.fname_metric_lst[m] = fname_out
def compute_length(fname_segmentation, remove_temp_files, verbose = 0):
from math import sqrt
# 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)
# copy files into tmp folder
sct.run('cp '+fname_segmentation+' '+path_tmp)
# go to tmp folder
os.chdir(path_tmp)
# Change orientation of the input centerline into RPI
sct.printv('\nOrient centerline to RPI orientation...', param.verbose)
im_seg = Image(file_data+ext_data)
fname_segmentation_orient = 'segmentation_rpi' + ext_data
im_seg_orient = set_orientation(im_seg, 'RPI')
im_seg_orient.setFileName(fname_segmentation_orient)
im_seg_orient.save()
# Get dimension
sct.printv('\nGet dimensions...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = im_seg_orient.dim
sct.printv('.. matrix size: '+str(nx)+' x '+str(ny)+' x '+str(nz), param.verbose)
sct.printv('.. voxel size: '+str(px)+'mm x '+str(py)+'mm x '+str(pz)+'mm', param.verbose)
# smooth segmentation/centerline
#x_centerline_fit, y_centerline_fit, z_centerline, x_centerline_deriv,y_centerline_deriv,z_centerline_deriv = smooth_centerline(fname_segmentation_orient, param, 'hanning', 1)
x_centerline_fit, y_centerline_fit, z_centerline, x_centerline_deriv,y_centerline_deriv,z_centerline_deriv = smooth_centerline(fname_segmentation_orient, type_window='hanning', window_length=80, algo_fitting='hanning', verbose = verbose)
# compute length of centerline
result_length = 0.0
for i in range(len(x_centerline_fit)-1):
result_length += sqrt(((x_centerline_fit[i+1]-x_centerline_fit[i])*px)**2+((y_centerline_fit[i+1]-y_centerline_fit[i])*py)**2+((z_centerline[i+1]-z_centerline[i])*pz)**2)
return result_length
def load_manual_gmseg(list_slices_target, list_fname_manual_gmseg, tmp_dir, im_sc_seg_rpi, new_res, square_size_size_mm, for_model=False):
if isinstance(list_fname_manual_gmseg, str):
# consider fname_manual_gmseg as a list of file names to allow multiple manual GM segmentation
list_fname_manual_gmseg = [list_fname_manual_gmseg]
for fname_manual_gmseg in list_fname_manual_gmseg:
os.chdir('..')
shutil.copy(fname_manual_gmseg, tmp_dir)
# change fname level to only file name (path = tmp dir now)
path_gm, file_gm, ext_gm = extract_fname(fname_manual_gmseg)
fname_manual_gmseg = file_gm + ext_gm
os.chdir(tmp_dir)
im_manual_gmseg = Image(fname_manual_gmseg)
# reorient to RPI
im_manual_gmseg = set_orientation(im_manual_gmseg, 'RPI')
# assert gmseg has the right number of slices
assert im_manual_gmseg.data.shape[2] == len(list_slices_target), 'ERROR: the manual GM segmentation has not the same number of slices than the image.'
# interpolate gm to reference image
nz_gmseg, nx_gmseg, ny_gmseg, nt_gmseg, pz_gmseg, px_gmseg, py_gmseg, pt_gmseg = im_manual_gmseg.dim
list_im_gm = interpolate_im_to_ref(im_manual_gmseg, im_sc_seg_rpi, new_res=new_res, sq_size_size_mm=square_size_size_mm, interpolation_mode=0)
# load gm seg in list of slices
n_poped=0
for im_gm, slice_im in zip(list_im_gm, list_slices_target):
if im_gm.data.max() == 0 and for_model:
list_slices_target.pop(slice_im.id-n_poped)
n_poped += 1
else:
slice_im.gm_seg.append(im_gm.data)
wm_slice = (slice_im.im > 0) - im_gm.data
slice_im.wm_seg.append(wm_slice)
return list_slices_target
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_properties_along_centerline(fname_seg_image,
property_list,
fname_disks_image=None,
smooth_factor=5.0,
interpolation_mode=0,
remove_temp_files=1,
verbose=1):
# Check list of properties
# If diameters is in the list, compute major and minor axis length and check orientation
compute_diameters = False
property_list_local = list(property_list)
if 'diameters' in property_list_local:
compute_diameters = True
property_list_local.remove('diameters')
property_list_local.append('major_axis_length')
property_list_local.append('minor_axis_length')
property_list_local.append('orientation')
# TODO: make sure fname_segmentation and fname_disks are in the same space
# create temporary folder and copying data
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)
sct.run('cp ' + fname_seg_image + ' ' + path_tmp)
if fname_disks_image is not None:
sct.run('cp ' + fname_disks_image + ' ' + path_tmp)
# go to tmp folder
os.chdir(path_tmp)
fname_segmentation = os.path.abspath(fname_seg_image)
path_data, file_data, ext_data = sct.extract_fname(fname_segmentation)
# Change orientation of the input centerline into RPI
sct.printv('\nOrient centerline to RPI orientation...', verbose)
im_seg = Image(file_data + ext_data)
fname_segmentation_orient = 'segmentation_rpi' + ext_data
image = set_orientation(im_seg, 'RPI')
image.setFileName(fname_segmentation_orient)
image.save()
# Initiating some variables
nx, ny, nz, nt, px, py, pz, pt = image.dim
resolution = 0.5
properties = {key: [] for key in property_list_local}
properties['incremental_length'] = []
properties['distance_from_C1'] = []
properties['vertebral_level'] = []
properties['z_slice'] = []
# compute the spinal cord centerline based on the spinal cord segmentation
number_of_points = 5 * nz
x_centerline_fit, y_centerline_fit, z_centerline, x_centerline_deriv, y_centerline_deriv, z_centerline_deriv = smooth_centerline(
fname_segmentation_orient,
algo_fitting='nurbs',
verbose=verbose,
nurbs_pts_number=number_of_points,
all_slices=False,
phys_coordinates=True,
remove_outliers=True)
centerline = Centerline(x_centerline_fit, y_centerline_fit, z_centerline,
x_centerline_deriv, y_centerline_deriv,
z_centerline_deriv)
# Compute vertebral distribution along centerline based on position of intervertebral disks
if fname_disks_image is not None:
fname_disks = os.path.abspath(fname_disks_image)
path_data, file_data, ext_data = sct.extract_fname(fname_disks)
im_disks = Image(file_data + ext_data)
fname_disks_orient = 'disks_rpi' + ext_data
image_disks = set_orientation(im_disks, 'RPI')
image_disks.setFileName(fname_disks_orient)
image_disks.save()
image_disks = Image(fname_disks_orient)
coord = image_disks.getNonZeroCoordinates(sorting='z',
reverse_coord=True)
coord_physical = []
for c in coord:
c_p = image_disks.transfo_pix2phys([[c.x, c.y, c.z]])[0]
c_p.append(c.value)
coord_physical.append(c_p)
centerline.compute_vertebral_distribution(coord_physical)
sct.printv('Computing spinal cord shape along the spinal cord...')
timer_properties = sct.Timer(
number_of_iteration=centerline.number_of_points)
timer_properties.start()
# Extracting patches perpendicular to the spinal cord and computing spinal cord shape
for index in range(centerline.number_of_points):
# value_out = -5.0
value_out = 0.0
current_patch = centerline.extract_perpendicular_square(
image,
index,
resolution=resolution,
interpolation_mode=interpolation_mode,
border='constant',
cval=value_out)
# check for pixels close to the spinal cord segmentation that are out of the image
from skimage.morphology import dilation
patch_zero = np.copy(current_patch)
patch_zero[patch_zero == value_out] = 0.0
patch_borders = dilation(patch_zero) - patch_zero
"""
if np.count_nonzero(patch_borders + current_patch == value_out + 1.0) != 0:
c = image.transfo_phys2pix([centerline.points[index]])[0]
print 'WARNING: no patch for slice', c[2]
timer_properties.add_iteration()
continue
"""
sc_properties = properties2d(patch_zero, [resolution, resolution])
if sc_properties is not None:
properties['incremental_length'].append(
centerline.incremental_length[index])
if fname_disks_image is not None:
properties['distance_from_C1'].append(
centerline.dist_points[index])
properties['vertebral_level'].append(
centerline.l_points[index])
properties['z_slice'].append(
image.transfo_phys2pix([centerline.points[index]])[0][2])
for property_name in property_list_local:
properties[property_name].append(sc_properties[property_name])
else:
c = image.transfo_phys2pix([centerline.points[index]])[0]
print 'WARNING: no properties for slice', c[2]
timer_properties.add_iteration()
timer_properties.stop()
# Adding centerline to the properties for later use
properties['centerline'] = centerline
# We assume that the major axis is in the right-left direction
# this script checks the orientation of the spinal cord and invert axis if necessary to make sure the major axis is right-left
if compute_diameters:
diameter_major = properties['major_axis_length']
diameter_minor = properties['minor_axis_length']
orientation = properties['orientation']
for i, orientation_item in enumerate(orientation):
if -45.0 < orientation_item < 45.0:
continue
else:
temp = diameter_minor[i]
properties['minor_axis_length'][i] = diameter_major[i]
properties['major_axis_length'][i] = temp
properties['RL_diameter'] = properties['major_axis_length']
properties['AP_diameter'] = properties['minor_axis_length']
del properties['major_axis_length']
del properties['minor_axis_length']
# smooth the spinal cord shape with a gaussian kernel if required
# TODO: not all properties can be smoothed
if smooth_factor != 0.0: # smooth_factor is in mm
import scipy
window = scipy.signal.hann(smooth_factor /
np.mean(centerline.progressive_length))
for property_name in property_list_local:
properties[property_name] = scipy.signal.convolve(
properties[property_name], window,
mode='same') / np.sum(window)
if compute_diameters:
property_list_local.remove('major_axis_length')
property_list_local.remove('minor_axis_length')
property_list_local.append('RL_diameter')
property_list_local.append('AP_diameter')
property_list = property_list_local
# Display properties on the referential space. Requires intervertebral disks
if verbose == 2:
x_increment = 'distance_from_C1'
if fname_disks_image is None:
x_increment = 'incremental_length'
# Display the image and plot all contours found
fig, axes = plt.subplots(len(property_list_local),
sharex=True,
sharey=False)
for k, property_name in enumerate(property_list_local):
axes[k].plot(properties[x_increment], properties[property_name])
axes[k].set_ylabel(property_name)
if fname_disks_image is not None:
properties[
'distance_disk_from_C1'] = centerline.distance_from_C1label # distance between each disk and C1 (or first disk)
xlabel_disks = [
centerline.convert_vertlabel2disklabel[label]
for label in properties['distance_disk_from_C1']
]
xtick_disks = [
properties['distance_disk_from_C1'][label]
for label in properties['distance_disk_from_C1']
]
plt.xticks(xtick_disks, xlabel_disks, rotation=30)
else:
axes[-1].set_xlabel('Position along the spinal cord (in mm)')
plt.show()
# Removing temporary folder
os.chdir('..')
shutil.rmtree(path_tmp, ignore_errors=True)
return property_list, properties
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 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 post_processing(self):
# DO INTERPOLATION BACK TO ORIGINAL IMAGE
# get original SC segmentation oriented in RPI
im_sc_seg_original_rpi = self.info_preprocessing['im_sc_seg_rpi'].copy()
nx_ref, ny_ref, nz_ref, nt_ref, px_ref, py_ref, pz_ref, pt_ref = im_sc_seg_original_rpi.dim
# create res GM seg image
im_res_gmseg = im_sc_seg_original_rpi.copy()
im_res_gmseg.data = np.zeros(im_res_gmseg.data.shape)
printv(' Interpolate result back into original space...', self.param.verbose, 'normal')
for iz, im_iz_preprocessed in enumerate(self.info_preprocessing['interpolated_images']):
# im gmseg for slice iz
im_gmseg = im_iz_preprocessed.copy()
im_gmseg.data = np.zeros(im_gmseg.data.shape)
im_gmseg.data = self.target_im[iz].gm_seg
im_res_slice, im_res_tot = (im_gmseg, im_res_gmseg)
# get reference image for this slice
# (use only one slice to accelerate interpolation)
im_ref = im_sc_seg_original_rpi.copy()
im_ref.data = im_ref.data[:, :, iz]
im_ref.dim = (nx_ref, ny_ref, 1, nt_ref, px_ref, py_ref, pz_ref, pt_ref)
# correct reference header for this slice
[[x_0_ref, y_0_ref, z_0_ref]] = im_ref.transfo_pix2phys(coordi=[[0, 0, iz]])
im_ref.hdr.as_analyze_map()['qoffset_x'] = x_0_ref
im_ref.hdr.as_analyze_map()['qoffset_y'] = y_0_ref
im_ref.hdr.as_analyze_map()['qoffset_z'] = z_0_ref
im_ref.hdr.set_sform(im_ref.hdr.get_qform())
im_ref.hdr.set_qform(im_ref.hdr.get_qform())
# set im_res_slice header with im_sc_seg_original_rpi origin
im_res_slice.hdr.as_analyze_map()['qoffset_x'] = x_0_ref
im_res_slice.hdr.as_analyze_map()['qoffset_y'] = y_0_ref
im_res_slice.hdr.as_analyze_map()['qoffset_z'] = z_0_ref
im_res_slice.hdr.set_sform(im_res_slice.hdr.get_qform())
im_res_slice.hdr.set_qform(im_res_slice.hdr.get_qform())
# get physical coordinates of center of sc
x_seg, y_seg = (im_sc_seg_original_rpi.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_sc_seg_original_rpi.transfo_pix2phys(coordi=[[x_center, y_center, iz]])
# get physical coordinates of center of square WITH im_res_slice WITH SAME ORIGIN AS im_sc_seg_original_rpi
sq_size_pix = int(self.param_data.square_size_size_mm / self.param_data.axial_res)
[[x_square_center_phys, y_square_center_phys, z_square_center_phys]] = im_res_slice.transfo_pix2phys(
coordi=[[int(sq_size_pix / 2), int(sq_size_pix / 2), 0]])
# set im_res_slice header by adding center of SC and center of square (in the correct space) to origin
im_res_slice.hdr.as_analyze_map()['qoffset_x'] += x_center_phys - x_square_center_phys
im_res_slice.hdr.as_analyze_map()['qoffset_y'] += y_center_phys - y_square_center_phys
im_res_slice.hdr.as_analyze_map()['qoffset_z'] += z_center_phys
im_res_slice.hdr.set_sform(im_res_slice.hdr.get_qform())
im_res_slice.hdr.set_qform(im_res_slice.hdr.get_qform())
# reshape data
im_res_slice.data = im_res_slice.data.reshape((sq_size_pix, sq_size_pix, 1))
# interpolate to reference image
interp = 1
im_res_slice_interp = im_res_slice.interpolate_from_image(im_ref, interpolation_mode=interp, border='nearest')
# set correct slice of total image with this slice
if len(im_res_slice_interp.data.shape) == 3:
shape_x, shape_y, shape_z = im_res_slice_interp.data.shape
im_res_slice_interp.data = im_res_slice_interp.data.reshape((shape_x, shape_y))
im_res_tot.data[:, :, iz] = im_res_slice_interp.data
if self.param_seg.type_seg == 'bin':
# binarize GM seg
data_gm = im_res_gmseg.data
data_gm[data_gm >= self.param_seg.thr_bin] = 1
data_gm[data_gm < self.param_seg.thr_bin] = 0
im_res_gmseg.data = data_gm
# create res WM seg image from GM and SC
im_res_wmseg = im_sc_seg_original_rpi.copy()
im_res_wmseg.data = im_res_wmseg.data - im_res_gmseg.data
# Put res back in original orientation
printv(' Reorient resulting segmentations to native orientation...', self.param.verbose, 'normal')
fname_gmseg = 'res_gmseg.nii.gz'
fname_wmseg = 'res_wmseg.nii.gz'
im_res_gmseg.setFileName(fname_gmseg)
im_res_gmseg.save()
im_res_wmseg.setFileName(fname_wmseg)
im_res_wmseg.save()
im_res_gmseg = set_orientation(im_res_gmseg, self.info_preprocessing['orientation'])
im_res_wmseg = set_orientation(im_res_wmseg, self.info_preprocessing['orientation'])
return im_res_gmseg, im_res_wmseg
def compute_ratio(self):
type_ratio = self.param_seg.ratio
tmp_dir_ratio = 'tmp_ratio/'
os.mkdir(tmp_dir_ratio)
os.chdir(tmp_dir_ratio)
fname_gmseg = self.im_res_gmseg.absolutepath
fname_wmseg = self.im_res_wmseg.absolutepath
self.im_res_gmseg.save()
self.im_res_wmseg.save()
if self.im_res_gmseg.orientation is not 'RPI':
im_res_gmseg = set_orientation(self.im_res_gmseg, 'RPI')
im_res_wmseg = set_orientation(self.im_res_wmseg, 'RPI')
fname_gmseg = im_res_gmseg.absolutepath
fname_wmseg = im_res_wmseg.absolutepath
#sct_process_segmentation.main(['-i', fname_gmseg, '-p', 'csa', '-ofolder', 'gm_csa'])
run('sct_process_segmentation -i ' + fname_gmseg + ' -p csa -ofolder gm_csa')
#sct_process_segmentation.main(['-i', fname_wmseg, '-p', 'csa', '-ofolder', 'wm_csa'])
run('sct_process_segmentation -i ' + fname_wmseg + ' -p csa -ofolder wm_csa')
gm_csa = open('gm_csa/csa_per_slice.txt', 'r')
wm_csa = open('wm_csa/csa_per_slice.txt', 'r')
gm_csa_lines = gm_csa.readlines()
wm_csa_lines = wm_csa.readlines()
gm_csa.close()
wm_csa.close()
fname_ratio = 'ratio_by_'+type_ratio+'.txt'
file_ratio = open(fname_ratio, 'w')
file_ratio.write(type_ratio + ', ratio GM/WM CSA\n')
csa_gm_wm_by_level = {0: [], 1: [], 2: [], 3: [], 4: [], 5: [], 6: [], 7: [], 8: [], 9: [], 10: [], 11: [], 12: [], 13: [], 14: [], 15: [], 16: [], 17: [], 18: [], 19: [], 20: [], 21: [], 22: [], 23: [], 24: []}
for gm_line, wm_line in zip(gm_csa_lines[1:], wm_csa_lines[1:]):
i, gm_area, gm_angle = gm_line.split(',')
j, wm_area, wm_angle = wm_line.split(',')
assert i == j
if type_ratio == 'level':
level_slice = int(self.target_im[int(i)].level)
csa_gm_wm_by_level[level_slice].append((float(gm_area), float(wm_area)))
else:
file_ratio.write(i + ', ' + str(float(gm_area) / float(wm_area)) + '\n')
if type_ratio == 'level':
for l, gm_wm_list in sorted(csa_gm_wm_by_level.items()):
if str(gm_wm_list) != '[]':
csa_gm_list = []
csa_wm_list = []
for gm, wm in gm_wm_list:
csa_gm_list.append(gm)
csa_wm_list.append(wm)
csa_gm = np.mean(csa_gm_list)
csa_wm = np.mean(csa_wm_list)
file_ratio.write(str(l) + ', ' + str(csa_gm / csa_wm) + '\n')
file_ratio.close()
shutil.copy(fname_ratio, '../../'+self.param_seg.path_results+'/'+fname_ratio)
os.chdir('..')
def __init__(self, im1, im2=None, param=None):
self.im1 = im1
self.im2 = im2
self.dim_im = len(self.im1.data.shape)
self.dim_pix = 0
self.distances = None
self.res = ''
self.param = param
self.dist1_distribution = None
self.dist2_distribution = None
if self.dim_im == 3:
self.orientation1 = self.im1.orientation
if self.orientation1 != 'IRP':
self.im1 = set_orientation(self.im1, 'IRP')
if self.im2 is not None:
self.orientation2 = self.im2.orientation
if self.orientation2 != 'IRP':
self.im2 = set_orientation(self.im2, 'IRP')
if self.param.thinning:
self.thinning1 = Thinning(self.im1, self.param.verbose)
self.thinning1.thinned_image.save()
if self.im2 is not None:
self.thinning2 = Thinning(self.im2, self.param.verbose)
self.thinning2.thinned_image.save()
if self.dim_im == 2 and self.im2 is not None:
self.compute_dist_2im_2d()
if self.dim_im == 3:
if self.im2 is None:
self.compute_dist_1im_3d()
else:
self.compute_dist_2im_3d()
if self.dim_im == 2 and self.distances is not None:
self.dist1_distribution = self.distances.min_distances_1[
np.nonzero(self.distances.min_distances_1)]
self.dist2_distribution = self.distances.min_distances_2[
np.nonzero(self.distances.min_distances_2)]
if self.dim_im == 3:
self.dist1_distribution = []
self.dist2_distribution = []
for d in self.distances:
self.dist1_distribution.append(d.min_distances_1[np.nonzero(
d.min_distances_1)])
self.dist2_distribution.append(d.min_distances_2[np.nonzero(
d.min_distances_2)])
self.res = 'Hausdorff\'s distance - First relative Hausdorff\'s distance median - Second relative Hausdorff\'s distance median(all in mm)\n'
for i, d in enumerate(self.distances):
med1 = np.median(self.dist1_distribution[i])
med2 = np.median(self.dist2_distribution[i])
if self.im2 is None:
self.res += 'Slice ' + str(i) + ' - slice ' + str(
i + 1) + ': ' + str(
d.H * self.dim_pix) + ' - ' + str(
med1 * self.dim_pix) + ' - ' + str(
med2 * self.dim_pix) + ' \n'
else:
self.res += 'Slice ' + str(i) + ': ' + str(
d.H * self.dim_pix) + ' - ' + str(
med1 * self.dim_pix) + ' - ' + str(
med2 * self.dim_pix) + ' \n'
sct.printv(
'-----------------------------------------------------------------------------\n'
+ self.res, self.param.verbose, 'normal')
if self.param.verbose == 2:
self.show_results()
def __init__(self, im1, im2=None, param=None):
self.im1 = im1
self.im2 = im2
self.dim_im = len(self.im1.data.shape)
self.dim_pix = 0
self.distances = None
self.res = ''
self.param = param
self.dist1_distribution = None
self.dist2_distribution = None
if self.dim_im == 3:
self.orientation1 = self.im1.orientation
if self.orientation1 != 'IRP':
self.im1 = set_orientation(self.im1, 'IRP')
if self.im2 is not None:
self.orientation2 = self.im2.orientation
if self.orientation2 != 'IRP':
self.im2 = set_orientation(self.im2, 'IRP')
if self.param.thinning:
self.thinning1 = Thinning(self.im1, self.param.verbose)
self.thinning1.thinned_image.save()
if self.im2 is not None:
self.thinning2 = Thinning(self.im2, self.param.verbose)
self.thinning2.thinned_image.save()
if self.dim_im == 2 and self.im2 is not None:
self.compute_dist_2im_2d()
if self.dim_im == 3:
if self.im2 is None:
self.compute_dist_1im_3d()
else:
self.compute_dist_2im_3d()
if self.dim_im == 2 and self.distances is not None:
self.dist1_distribution = self.distances.min_distances_1[np.nonzero(self.distances.min_distances_1)]
self.dist2_distribution = self.distances.min_distances_2[np.nonzero(self.distances.min_distances_2)]
if self.dim_im == 3:
self.dist1_distribution = []
self.dist2_distribution = []
for d in self.distances:
self.dist1_distribution.append(d.min_distances_1[np.nonzero(d.min_distances_1)])
self.dist2_distribution.append(d.min_distances_2[np.nonzero(d.min_distances_2)])
self.res = 'Hausdorff\'s distance - First relative Hausdorff\'s distance median - Second relative Hausdorff\'s distance median(all in mm)\n'
for i, d in enumerate(self.distances):
med1 = np.median(self.dist1_distribution[i])
med2 = np.median(self.dist2_distribution[i])
if self.im2 is None:
self.res += 'Slice ' + str(i) + ' - slice ' + str(i+1) + ': ' + str(d.H*self.dim_pix) + ' - ' + str(med1*self.dim_pix) + ' - ' + str(med2*self.dim_pix) + ' \n'
else:
self.res += 'Slice ' + str(i) + ': ' + str(d.H*self.dim_pix) + ' - ' + str(med1*self.dim_pix) + ' - ' + str(med2*self.dim_pix) + ' \n'
sct.printv('-----------------------------------------------------------------------------\n' +
self.res, self.param.verbose, 'normal')
if self.param.verbose == 2:
self.show_results()
def main(fname_anat, fname_centerline, degree_poly, centerline_fitting, interp, remove_temp_files, verbose):
# extract path of the script
path_script = os.path.dirname(__file__)+'/'
# Parameters for debug mode
if param.debug == 1:
print '\n*** WARNING: DEBUG MODE ON ***\n'
status, path_sct_data = commands.getstatusoutput('echo $SCT_TESTING_DATA_DIR')
fname_anat = path_sct_data+'/t2/t2.nii.gz'
fname_centerline = path_sct_data+'/t2/t2_seg.nii.gz'
# extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
# Display arguments
print '\nCheck input arguments...'
print ' Input volume ...................... '+fname_anat
print ' Centerline ........................ '+fname_centerline
print ''
# Get input image orientation
im_anat = Image(fname_anat)
input_image_orientation = get_orientation(im_anat)
# Reorient input data into RL PA IS orientation
im_centerline = Image(fname_centerline)
im_anat_orient = set_orientation(im_anat, 'RPI')
im_anat_orient.setFileName('tmp.anat_orient.nii')
im_centerline_orient = set_orientation(im_centerline, 'RPI')
im_centerline_orient.setFileName('tmp.centerline_orient.nii')
# Open centerline
#==========================================================================================
print '\nGet dimensions of input centerline...'
nx, ny, nz, nt, px, py, pz, pt = im_centerline_orient.dim
print '.. matrix size: '+str(nx)+' x '+str(ny)+' x '+str(nz)
print '.. voxel size: '+str(px)+'mm x '+str(py)+'mm x '+str(pz)+'mm'
print '\nOpen centerline volume...'
data = im_centerline_orient.data
X, Y, Z = (data>0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
# loop across z and associate x,y coordinate with the point having maximum intensity
x_centerline = [0 for iz in range(min_z_index, max_z_index+1, 1)]
y_centerline = [0 for iz in range(min_z_index, max_z_index+1, 1)]
z_centerline = [iz for iz in range(min_z_index, max_z_index+1, 1)]
# Two possible scenario:
# 1. the centerline is probabilistic: each slices contains voxels with the probability of containing the centerline [0:...:1]
# We only take the maximum value of the image to aproximate the centerline.
# 2. The centerline/segmentation image contains many pixels per slice with values {0,1}.
# We take all the points and approximate the centerline on all these points.
X, Y, Z = ((data<1)*(data>0)).nonzero() # X is empty if binary image
if (len(X) > 0): # Scenario 1
for iz in range(min_z_index, max_z_index+1, 1):
x_centerline[iz-min_z_index], y_centerline[iz-min_z_index] = numpy.unravel_index(data[:,:,iz].argmax(), data[:,:,iz].shape)
else: # Scenario 2
for iz in range(min_z_index, max_z_index+1, 1):
x_seg, y_seg = (data[:,:,iz]>0).nonzero()
if len(x_seg) > 0:
x_centerline[iz-min_z_index] = numpy.mean(x_seg)
y_centerline[iz-min_z_index] = numpy.mean(y_seg)
# TODO: find a way to do the previous loop with this, which is more neat:
# [numpy.unravel_index(data[:,:,iz].argmax(), data[:,:,iz].shape) for iz in range(0,nz,1)]
# clear variable
del data
# Fit the centerline points with the kind of curve given as argument of the script and return the new smoothed coordinates
if centerline_fitting == 'nurbs':
try:
x_centerline_fit, y_centerline_fit = b_spline_centerline(x_centerline,y_centerline,z_centerline)
except ValueError:
print "splines fitting doesn't work, trying with polynomial fitting...\n"
x_centerline_fit, y_centerline_fit = polynome_centerline(x_centerline,y_centerline,z_centerline)
elif centerline_fitting == 'polynome':
x_centerline_fit, y_centerline_fit = polynome_centerline(x_centerline,y_centerline,z_centerline)
#==========================================================================================
# Split input volume
print '\nSplit input volume...'
im_anat_orient_split_list = split_data(im_anat_orient, 2)
file_anat_split = []
for im in im_anat_orient_split_list:
file_anat_split.append(im.absolutepath)
im.save()
# initialize variables
file_mat_inv_cumul = ['tmp.mat_inv_cumul_Z'+str(z).zfill(4) for z in range(0,nz,1)]
z_init = min_z_index
displacement_max_z_index = x_centerline_fit[z_init-min_z_index]-x_centerline_fit[max_z_index-min_z_index]
# write centerline as text file
print '\nGenerate fitted transformation matrices...'
file_mat_inv_cumul_fit = ['tmp.mat_inv_cumul_fit_Z'+str(z).zfill(4) for z in range(0,nz,1)]
for iz in range(min_z_index, max_z_index+1, 1):
# compute inverse cumulative fitted transformation matrix
fid = open(file_mat_inv_cumul_fit[iz], 'w')
if (x_centerline[iz-min_z_index] == 0 and y_centerline[iz-min_z_index] == 0):
displacement = 0
else:
displacement = x_centerline_fit[z_init-min_z_index]-x_centerline_fit[iz-min_z_index]
fid.write('%i %i %i %f\n' %(1, 0, 0, displacement) )
fid.write('%i %i %i %f\n' %(0, 1, 0, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 1, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 0, 1) )
fid.close()
# we complete the displacement matrix in z direction
for iz in range(0, min_z_index, 1):
fid = open(file_mat_inv_cumul_fit[iz], 'w')
fid.write('%i %i %i %f\n' %(1, 0, 0, 0) )
fid.write('%i %i %i %f\n' %(0, 1, 0, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 1, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 0, 1) )
fid.close()
for iz in range(max_z_index+1, nz, 1):
fid = open(file_mat_inv_cumul_fit[iz], 'w')
fid.write('%i %i %i %f\n' %(1, 0, 0, displacement_max_z_index) )
fid.write('%i %i %i %f\n' %(0, 1, 0, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 1, 0) )
fid.write('%i %i %i %i\n' %(0, 0, 0, 1) )
fid.close()
# apply transformations to data
print '\nApply fitted transformation matrices...'
file_anat_split_fit = ['tmp.anat_orient_fit_Z'+str(z).zfill(4) for z in range(0,nz,1)]
for iz in range(0, nz, 1):
# forward cumulative transformation to data
sct.run(fsloutput+'flirt -in '+file_anat_split[iz]+' -ref '+file_anat_split[iz]+' -applyxfm -init '+file_mat_inv_cumul_fit[iz]+' -out '+file_anat_split_fit[iz]+' -interp '+interp)
# Merge into 4D volume
print '\nMerge into 4D volume...'
from glob import glob
im_to_concat_list = [Image(fname) for fname in glob('tmp.anat_orient_fit_Z*.nii')]
im_concat_out = concat_data(im_to_concat_list, 2)
im_concat_out.setFileName('tmp.anat_orient_fit.nii')
im_concat_out.save()
# sct.run(fsloutput+'fslmerge -z tmp.anat_orient_fit tmp.anat_orient_fit_z*')
# Reorient data as it was before
print '\nReorient data back into native orientation...'
fname_anat_fit_orient = set_orientation(im_concat_out.absolutepath, input_image_orientation, filename=True)
move(fname_anat_fit_orient, 'tmp.anat_orient_fit_reorient.nii')
# Generate output file (in current folder)
print '\nGenerate output file (in current folder)...'
sct.generate_output_file('tmp.anat_orient_fit_reorient.nii', file_anat+'_flatten'+ext_anat)
# Delete temporary files
if remove_temp_files == 1:
print '\nDelete temporary files...'
sct.run('rm -rf tmp.*')
# to view results
print '\nDone! To view results, type:'
print 'fslview '+file_anat+ext_anat+' '+file_anat+'_flatten'+ext_anat+' &\n'
def main(args=None):
# Initialization
# fname_anat = ''
# fname_centerline = ''
sigma = 3 # default value of the standard deviation for the Gaussian smoothing (in terms of number of voxels)
# remove_temp_files = param.remove_temp_files
# verbose = param.verbose
start_time = time.time()
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
fname_anat = arguments['-i']
fname_centerline = arguments['-s']
if '-smooth' in arguments:
sigma = arguments['-smooth']
if '-r' in arguments:
remove_temp_files = int(arguments['-r'])
if '-v' in arguments:
verbose = int(arguments['-v'])
# Display arguments
sct.printv('\nCheck input arguments...')
sct.printv(' Volume to smooth .................. ' + fname_anat)
sct.printv(' Centerline ........................ ' + fname_centerline)
sct.printv(' Sigma (mm) ........................ ' + str(sigma))
sct.printv(' Verbose ........................... ' + str(verbose))
# Check that input is 3D:
from msct_image import Image
nx, ny, nz, nt, px, py, pz, pt = Image(fname_anat).dim
dim = 4 # by default, will be adjusted later
if nt == 1:
dim = 3
if nz == 1:
dim = 2
if dim == 4:
sct.printv('WARNING: the input image is 4D, please split your image to 3D before smoothing spinalcord using :\n'
'sct_image -i ' + fname_anat + ' -split t -o ' + fname_anat, verbose, 'warning')
sct.printv('4D images not supported, aborting ...', verbose, 'error')
# Extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
path_centerline, file_centerline, ext_centerline = sct.extract_fname(fname_centerline)
path_tmp = sct.tmp_create(basename="smooth_spinalcord", verbose=verbose)
# Copying input data to tmp folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
sct.copy(fname_anat, os.path.join(path_tmp, "anat" + ext_anat))
sct.copy(fname_centerline, os.path.join(path_tmp, "centerline" + ext_centerline))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# convert to nii format
convert('anat' + ext_anat, 'anat.nii')
convert('centerline' + ext_centerline, 'centerline.nii')
# Change orientation of the input image into RPI
sct.printv('\nOrient input volume to RPI orientation...')
fname_anat_rpi = set_orientation('anat.nii', 'RPI', filename=True)
shutil.move(fname_anat_rpi, 'anat_rpi.nii')
# Change orientation of the input image into RPI
sct.printv('\nOrient centerline to RPI orientation...')
fname_centerline_rpi = set_orientation('centerline.nii', 'RPI', filename=True)
shutil.move(fname_centerline_rpi, 'centerline_rpi.nii')
# Straighten the spinal cord
# straighten segmentation
sct.printv('\nStraighten the spinal cord using centerline/segmentation...', verbose)
cache_sig = sct.cache_signature(
input_files=["anat_rpi.nii", "centerline_rpi.nii"],
input_params={"x": "spline"},
)
cachefile = os.path.join(curdir, "straightening.cache")
if sct.cache_valid(cachefile, cache_sig) and os.path.isfile(os.path.join(curdir, 'warp_curve2straight.nii.gz')) and os.path.isfile(os.path.join(curdir, 'warp_straight2curve.nii.gz')) and os.path.isfile(os.path.join(curdir, 'straight_ref.nii.gz')):
# if they exist, copy them into current folder
sct.printv('Reusing existing warping field which seems to be valid', verbose, 'warning')
sct.copy(os.path.join(curdir, 'warp_curve2straight.nii.gz'), 'warp_curve2straight.nii.gz')
sct.copy(os.path.join(curdir, 'warp_straight2curve.nii.gz'), 'warp_straight2curve.nii.gz')
sct.copy(os.path.join(curdir, 'straight_ref.nii.gz'), 'straight_ref.nii.gz')
# apply straightening
sct.run(['sct_apply_transfo', '-i', 'anat_rpi.nii', '-w', 'warp_curve2straight.nii.gz', '-d', 'straight_ref.nii.gz', '-o', 'anat_rpi_straight.nii', '-x', 'spline'], verbose)
else:
sct.run(['sct_straighten_spinalcord', '-i', 'anat_rpi.nii', '-s', 'centerline_rpi.nii', '-x', 'spline'], verbose)
sct.cache_save(cachefile, cache_sig)
# Smooth the straightened image along z
sct.printv('\nSmooth the straightened image along z...')
sct.run(['sct_maths', '-i', 'anat_rpi_straight.nii', '-smooth', '0,0,' + str(sigma), '-o', 'anat_rpi_straight_smooth.nii'], verbose)
# Apply the reversed warping field to get back the curved spinal cord
sct.printv('\nApply the reversed warping field to get back the curved spinal cord...')
sct.run(['sct_apply_transfo', '-i', 'anat_rpi_straight_smooth.nii', '-o', 'anat_rpi_straight_smooth_curved.nii', '-d', 'anat.nii', '-w', 'warp_straight2curve.nii.gz', '-x', 'spline'], verbose)
# replace zeroed voxels by original image (issue #937)
sct.printv('\nReplace zeroed voxels by original image...', verbose)
nii_smooth = Image('anat_rpi_straight_smooth_curved.nii')
data_smooth = nii_smooth.data
data_input = Image('anat.nii').data
indzero = np.where(data_smooth == 0)
data_smooth[indzero] = data_input[indzero]
nii_smooth.data = data_smooth
nii_smooth.setFileName('anat_rpi_straight_smooth_curved_nonzero.nii')
nii_smooth.save()
# come back
os.chdir(curdir)
# Generate output file
sct.printv('\nGenerate output file...')
sct.generate_output_file(os.path.join(path_tmp, "anat_rpi_straight_smooth_curved_nonzero.nii"),
file_anat + '_smooth' + ext_anat)
# Remove temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...')
sct.rmtree(path_tmp)
# Display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(round(elapsed_time))) + 's\n')
sct.display_viewer_syntax([file_anat, file_anat + '_smooth'], verbose=verbose)
def main():
# initialization
fname_mask = ''
# Get parser info
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
fname_data = arguments['-i']
fname_mask = arguments['-m']
vert_label_fname = arguments["-vertfile"]
vert_levels = arguments["-vert"]
slices_of_interest = arguments["-z"]
method = arguments["-method"]
verbose = int(arguments['-v'])
# Check if data are in RPI
input_im = Image(fname_data)
input_orient = get_orientation_3d(input_im)
# If orientation is not RPI, change to RPI
if input_orient != 'RPI':
sct.printv('\nCreate temporary folder to change the orientation of the NIFTI files into RPI...', verbose)
path_tmp = sct.tmp_create()
# change orientation and load data
sct.printv('\nChange input image orientation and load it...', verbose)
input_im_rpi = set_orientation(input_im, 'RPI', fname_out=path_tmp+'input_RPI.nii')
input_data = input_im_rpi.data
# Do the same for the mask
sct.printv('\nChange mask orientation and load it...', verbose)
mask_im_rpi = set_orientation(Image(fname_mask), 'RPI', fname_out=path_tmp+'mask_RPI.nii')
mask_data = mask_im_rpi.data
# Do the same for vertebral labeling if present
if vert_levels != 'None':
sct.printv('\nChange vertebral labeling file orientation and load it...', verbose)
vert_label_im_rpi = set_orientation(Image(vert_label_fname), 'RPI', fname_out=path_tmp+'vert_labeling_RPI.nii')
vert_labeling_data = vert_label_im_rpi.data
# Remove the temporary folder used to change the NIFTI files orientation into RPI
sct.printv('\nRemove the temporary folder...', verbose)
rmdir(path_tmp)
else:
# Load data
sct.printv('\nLoad data...', verbose)
input_data = input_im.data
mask_data = Image(fname_mask).data
if vert_levels != 'None':
vert_labeling_data = Image(vert_label_fname).data
sct.printv('\tDone.', verbose)
# Get slices corresponding to vertebral levels
if vert_levels != 'None':
from sct_extract_metric import get_slices_matching_with_vertebral_levels
slices_of_interest, actual_vert_levels, warning_vert_levels = get_slices_matching_with_vertebral_levels(mask_data, vert_levels, vert_labeling_data, verbose)
# Remove slices that were not selected
if slices_of_interest == 'None':
slices_of_interest = '0:'+str(mask_data.shape[2]-1)
slices_boundary = slices_of_interest.split(':')
slices_of_interest_list = range(int(slices_boundary[0]), int(slices_boundary[1])+1)
# Crop
input_data = input_data[:, :, slices_of_interest_list, :]
mask_data = mask_data[:, :, slices_of_interest_list]
# Get signal and noise
indexes_roi = np.where(mask_data == 1)
if method == 'mult':
signal = np.mean(input_data[indexes_roi])
std_input_temporal = np.std(input_data, 3)
noise = np.mean(std_input_temporal[indexes_roi])
elif method == 'diff':
data_1 = input_data[:, :, :, 0]
data_2 = input_data[:, :, :, 1]
signal = np.mean(np.add(b0_1[indexes_roi], b0_2[indexes_roi]))
noise = np.sqrt(2)*np.std(np.subtract(b0_1[indexes_roi], b0_2[indexes_roi]))
elif method == 'background':
sct.printv('ERROR: Sorry, method is not implemented yet.', 1, 'error')
elif method == 'nema':
sct.printv('ERROR: Sorry, method is not implemented yet.', 1, 'error')
# compute SNR
SNR = signal/noise
# Display result
sct.printv('\nSNR_'+method+' = '+str(SNR)+'\n', type='info')
def pre_processing(fname_target,
fname_sc_seg,
fname_level=None,
fname_manual_gmseg=None,
new_res=0.3,
square_size_size_mm=22.5,
denoising=True,
verbose=1,
rm_tmp=True,
for_model=False):
printv('\nPre-process data...', verbose, 'normal')
tmp_dir = sct.tmp_create()
sct.copy(fname_target, tmp_dir)
fname_target = ''.join(extract_fname(fname_target)[1:])
sct.copy(fname_sc_seg, tmp_dir)
fname_sc_seg = ''.join(extract_fname(fname_sc_seg)[1:])
curdir = os.getcwd()
os.chdir(tmp_dir)
original_info = {
'orientation': None,
'im_sc_seg_rpi': None,
'interpolated_images': []
}
im_target = Image(fname_target).copy()
im_sc_seg = Image(fname_sc_seg).copy()
# get original orientation
printv(' Reorient...', verbose, 'normal')
original_info['orientation'] = im_target.orientation
# assert images are in the same orientation
assert im_target.orientation == im_sc_seg.orientation, "ERROR: the image to segment and it's SC segmentation are not in the same orientation"
im_target_rpi = set_orientation(im_target, 'RPI')
im_sc_seg_rpi = set_orientation(im_sc_seg, 'RPI')
original_info['im_sc_seg_rpi'] = im_sc_seg_rpi.copy(
) # target image in RPI will be used to post-process segmentations
# denoise using P. Coupe non local means algorithm (see [Manjon et al. JMRI 2010]) implemented in dipy
if denoising:
printv(' Denoise...', verbose, 'normal')
# crop image before denoising to fasten denoising
nx, ny, nz, nt, px, py, pz, pt = im_target_rpi.dim
size_x, size_y = (square_size_size_mm + 1) / px, (square_size_size_mm +
1) / py
size = int(math.ceil(max(size_x, size_y)))
# create mask
fname_mask = 'mask_pre_crop.nii.gz'
sct_create_mask.main([
'-i', im_target_rpi.absolutepath, '-p',
'centerline,' + im_sc_seg_rpi.absolutepath, '-f', 'box', '-size',
str(size), '-o', fname_mask
])
# crop image
fname_target_crop = add_suffix(im_target_rpi.absolutepath, '_pre_crop')
crop_im = ImageCropper(input_file=im_target_rpi.absolutepath,
output_file=fname_target_crop,
mask=fname_mask)
im_target_rpi_crop = crop_im.crop()
# crop segmentation
fname_sc_seg_crop = add_suffix(im_sc_seg_rpi.absolutepath, '_pre_crop')
crop_sc_seg = ImageCropper(input_file=im_sc_seg_rpi.absolutepath,
output_file=fname_sc_seg_crop,
mask=fname_mask)
im_sc_seg_rpi_crop = crop_sc_seg.crop()
# denoising
from sct_maths import denoise_nlmeans
block_radius = 3
block_radius = int(
im_target_rpi_crop.data.shape[2] /
2) if im_target_rpi_crop.data.shape[2] < (block_radius *
2) else block_radius
patch_radius = block_radius - 1
data_denoised = denoise_nlmeans(im_target_rpi_crop.data,
block_radius=block_radius,
patch_radius=patch_radius)
im_target_rpi_crop.data = data_denoised
im_target_rpi = im_target_rpi_crop
im_sc_seg_rpi = im_sc_seg_rpi_crop
else:
fname_mask = None
# interpolate image to reference square image (resample and square crop centered on SC)
printv(' Interpolate data to the model space...', verbose, 'normal')
list_im_slices = interpolate_im_to_ref(im_target_rpi,
im_sc_seg_rpi,
new_res=new_res,
sq_size_size_mm=square_size_size_mm)
original_info[
'interpolated_images'] = list_im_slices # list of images (not Slice() objects)
printv(' Mask data using the spinal cord segmentation...', verbose,
'normal')
list_sc_seg_slices = interpolate_im_to_ref(
im_sc_seg_rpi,
im_sc_seg_rpi,
new_res=new_res,
sq_size_size_mm=square_size_size_mm,
interpolation_mode=1)
for i in range(len(list_im_slices)):
# list_im_slices[i].data[list_sc_seg_slices[i].data == 0] = 0
list_sc_seg_slices[i] = binarize(list_sc_seg_slices[i],
thr_min=0.5,
thr_max=1)
list_im_slices[
i].data = list_im_slices[i].data * list_sc_seg_slices[i].data
printv(' Split along rostro-caudal direction...', verbose, 'normal')
list_slices_target = [
Slice(slice_id=i, im=im_slice.data, gm_seg=[], wm_seg=[])
for i, im_slice in enumerate(list_im_slices)
]
# load vertebral levels
if fname_level is not None:
printv(' Load vertebral levels...', verbose, 'normal')
# copy level file to tmp dir
os.chdir(curdir)
sct.copy(fname_level, tmp_dir)
os.chdir(tmp_dir)
# change fname level to only file name (path = tmp dir now)
fname_level = ''.join(extract_fname(fname_level)[1:])
# load levels
list_slices_target = load_level(list_slices_target, fname_level)
os.chdir(curdir)
# load manual gmseg if there is one (model data)
if fname_manual_gmseg is not None:
printv('\n\tLoad manual GM segmentation(s) ...', verbose, 'normal')
list_slices_target = load_manual_gmseg(list_slices_target,
fname_manual_gmseg,
tmp_dir,
im_sc_seg_rpi,
new_res,
square_size_size_mm,
for_model=for_model,
fname_mask=fname_mask)
if rm_tmp:
# remove tmp folder
sct.rmtree(tmp_dir)
return list_slices_target, original_info
def _orient(self, fname, orientation):
im = Image(fname)
im = set_orientation(im, orientation, fname_out=fname)
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 pre_processing(fname_target, fname_sc_seg, fname_level=None, fname_manual_gmseg=None, new_res=0.3, square_size_size_mm=22.5, denoising=True, verbose=1, rm_tmp=True, for_model=False):
printv('\nPre-process data...', verbose, 'normal')
tmp_dir = 'tmp_preprocessing_' + time.strftime("%y%m%d%H%M%S") + '_' + str(random.randint(1, 1000000)) + '/'
if not os.path.exists(tmp_dir):
os.mkdir(tmp_dir)
shutil.copy(fname_target, tmp_dir)
fname_target = ''.join(extract_fname(fname_target)[1:])
shutil.copy(fname_sc_seg, tmp_dir)
fname_sc_seg = ''.join(extract_fname(fname_sc_seg)[1:])
os.chdir(tmp_dir)
original_info = {'orientation': None, 'im_sc_seg_rpi': None, 'interpolated_images': []}
im_target = Image(fname_target).copy()
im_sc_seg = Image(fname_sc_seg).copy()
# get original orientation
printv(' Reorient...', verbose, 'normal')
original_info['orientation'] = im_target.orientation
# assert images are in the same orientation
assert im_target.orientation == im_sc_seg.orientation, "ERROR: the image to segment and it's SC segmentation are not in the same orientation"
im_target_rpi = set_orientation(im_target, 'RPI')
im_sc_seg_rpi = set_orientation(im_sc_seg, 'RPI')
original_info['im_sc_seg_rpi'] = im_sc_seg_rpi.copy() # target image in RPI will be used to post-process segmentations
# interpolate image to reference square image (resample and square crop centered on SC)
printv(' Interpolate data to the model space...', verbose, 'normal')
list_im_slices = interpolate_im_to_ref(im_target_rpi, im_sc_seg_rpi, new_res=new_res, sq_size_size_mm=square_size_size_mm)
original_info['interpolated_images'] = list_im_slices # list of images (not Slice() objects)
# denoise using P. Coupe non local means algorithm (see [Manjon et al. JMRI 2010]) implemented in dipy
if denoising:
printv(' Denoise...', verbose, 'normal')
from sct_maths import denoise_nlmeans
data = np.asarray([im.data for im in list_im_slices])
data_denoised = denoise_nlmeans(data, block_radius = int(len(list_im_slices)/2))
for i in range(len(list_im_slices)):
list_im_slices[i].data = data_denoised[i]
printv(' Mask data using the spinal cord segmentation...', verbose, 'normal')
list_sc_seg_slices = interpolate_im_to_ref(im_sc_seg_rpi, im_sc_seg_rpi, new_res=new_res, sq_size_size_mm=square_size_size_mm, interpolation_mode=1)
for i in range(len(list_im_slices)):
# list_im_slices[i].data[list_sc_seg_slices[i].data == 0] = 0
list_sc_seg_slices[i] = binarize(list_sc_seg_slices[i], thr_min=0.5, thr_max=1)
list_im_slices[i].data = list_im_slices[i].data * list_sc_seg_slices[i].data
printv(' Split along rostro-caudal direction...', verbose, 'normal')
list_slices_target = [Slice(slice_id=i, im=im_slice.data, gm_seg=[], wm_seg=[]) for i, im_slice in enumerate(list_im_slices)]
# load vertebral levels
if fname_level is not None:
printv(' Load vertebral levels...', verbose, 'normal')
# copy level file to tmp dir
os.chdir('..')
shutil.copy(fname_level, tmp_dir)
os.chdir(tmp_dir)
# change fname level to only file name (path = tmp dir now)
fname_level = ''.join(extract_fname(fname_level)[1:])
# load levels
list_slices_target = load_level(list_slices_target, fname_level)
# load manual gmseg if there is one (model data)
if fname_manual_gmseg is not None:
printv('\n\tLoad manual GM segmentation(s) ...', verbose, 'normal')
list_slices_target = load_manual_gmseg(list_slices_target, fname_manual_gmseg, tmp_dir, im_sc_seg_rpi, new_res, square_size_size_mm, for_model=for_model)
os.chdir('..')
if rm_tmp:
# remove tmp folder
shutil.rmtree(tmp_dir)
return list_slices_target, original_info
def deep_segmentation_spinalcord(fname_image,
contrast_type,
output_folder,
ctr_algo='cnn',
brain_bool=True,
kernel_size='2d',
remove_temp_files=1,
verbose=1):
"""Pipeline."""
path_script = os.path.dirname(__file__)
path_sct = os.path.dirname(path_script)
# create temporary folder with intermediate results
sct.log.info("Creating temporary folder...")
file_fname = os.path.basename(fname_image)
tmp_folder = sct.TempFolder()
tmp_folder_path = tmp_folder.get_path()
fname_image_tmp = tmp_folder.copy_from(fname_image)
tmp_folder.chdir()
# orientation of the image, should be RPI
sct.log.info("Reorient the image to RPI, if necessary...")
fname_orient = sct.add_suffix(file_fname, '_RPI')
im_2orient = Image(file_fname)
original_orientation = im_2orient.orientation
if original_orientation != 'RPI':
im_orient = set_orientation(im_2orient, 'RPI')
im_orient.setFileName(fname_orient)
im_orient.save()
else:
im_orient = im_2orient
sct.copy(fname_image_tmp, fname_orient)
# resampling RPI image
sct.log.info("Resample the image to 0.5 mm isotropic resolution...")
fname_res = sct.add_suffix(fname_orient, '_resampled')
im_2res = im_orient
input_resolution = im_2res.dim[4:7]
new_resolution = 'x'.join(['0.5', '0.5', str(input_resolution[2])])
spinalcordtoolbox.resample.nipy_resample.resample_file(fname_orient,
fname_res,
new_resolution,
'mm',
'linear',
verbose=0)
# find the spinal cord centerline - execute OptiC binary
sct.log.info("Finding the spinal cord centerline...")
if ctr_algo == 'svm':
# run optic on a heatmap computed by a trained SVM+HoG algorithm
optic_models_fname = os.path.join(path_sct, 'data', 'optic_models',
'{}_model'.format(contrast_type))
_, centerline_filename = optic.detect_centerline(
image_fname=fname_res,
contrast_type=contrast_type,
optic_models_path=optic_models_fname,
folder_output=tmp_folder_path,
remove_temp_files=remove_temp_files,
output_roi=False,
verbose=0)
elif ctr_algo == 'cnn':
# CNN parameters
dct_patch_ctr = {
't2': {
'size': (80, 80),
'mean': 51.1417,
'std': 57.4408
},
't2s': {
'size': (80, 80),
'mean': 68.8591,
'std': 71.4659
},
't1': {
'size': (80, 80),
'mean': 55.7359,
'std': 64.3149
},
'dwi': {
'size': (80, 80),
'mean': 55.744,
'std': 45.003
}
}
dct_params_ctr = {
't2': {
'features': 16,
'dilation_layers': 2
},
't2s': {
'features': 8,
'dilation_layers': 3
},
't1': {
'features': 24,
'dilation_layers': 3
},
'dwi': {
'features': 8,
'dilation_layers': 2
}
}
# load model
ctr_model_fname = os.path.join(path_sct, 'data', 'deepseg_sc_models',
'{}_ctr.h5'.format(contrast_type))
ctr_model = nn_architecture_ctr(
height=dct_patch_ctr[contrast_type]['size'][0],
width=dct_patch_ctr[contrast_type]['size'][1],
channels=1,
classes=1,
features=dct_params_ctr[contrast_type]['features'],
depth=2,
temperature=1.0,
padding='same',
batchnorm=True,
dropout=0.0,
dilation_layers=dct_params_ctr[contrast_type]['dilation_layers'])
ctr_model.load_weights(ctr_model_fname)
# compute the heatmap
fname_heatmap = sct.add_suffix(fname_res, "_heatmap")
img_filename = ''.join(sct.extract_fname(fname_heatmap)[:2])
fname_heatmap_nii = img_filename + '.nii'
z_max = heatmap(filename_in=fname_res,
filename_out=fname_heatmap_nii,
model=ctr_model,
patch_shape=dct_patch_ctr[contrast_type]['size'],
mean_train=dct_patch_ctr[contrast_type]['mean'],
std_train=dct_patch_ctr[contrast_type]['std'],
brain_bool=brain_bool)
# run optic on the heatmap
centerline_filename = sct.add_suffix(fname_heatmap, "_ctr")
heatmap2optic(fname_heatmap=fname_heatmap_nii,
lambda_value=7 if contrast_type == 't2s' else 1,
fname_out=centerline_filename,
z_max=z_max if brain_bool else None)
# crop image around the spinal cord centerline
sct.log.info("Cropping the image around the spinal cord...")
fname_crop = sct.add_suffix(fname_res, '_crop')
crop_size = 64 if kernel_size == '2d' else 96
X_CROP_LST, Y_CROP_LST = crop_image_around_centerline(
filename_in=fname_res,
filename_ctr=centerline_filename,
filename_out=fname_crop,
crop_size=crop_size)
# normalize the intensity of the images
sct.log.info("Normalizing the intensity...")
fname_norm = sct.add_suffix(fname_crop, '_norm')
apply_intensity_normalization(img_path=fname_crop, fname_out=fname_norm)
if kernel_size == '2d':
# segment data using 2D convolutions
sct.log.info(
"Segmenting the spinal cord using deep learning on 2D patches...")
segmentation_model_fname = os.path.join(
path_sct, 'data', 'deepseg_sc_models',
'{}_sc.h5'.format(contrast_type))
fname_seg_crop = sct.add_suffix(fname_norm, '_seg')
seg_crop_data = segment_2d(model_fname=segmentation_model_fname,
contrast_type=contrast_type,
input_size=(crop_size, crop_size),
fname_in=fname_norm,
fname_out=fname_seg_crop)
elif kernel_size == '3d':
# resample to 0.5mm isotropic
fname_res3d = sct.add_suffix(fname_norm, '_resampled3d')
spinalcordtoolbox.resample.nipy_resample.resample_file(fname_norm,
fname_res3d,
'0.5x0.5x0.5',
'mm',
'linear',
verbose=0)
# segment data using 3D convolutions
sct.log.info(
"Segmenting the spinal cord using deep learning on 3D patches...")
segmentation_model_fname = os.path.join(
path_sct, 'data', 'deepseg_sc_models',
'{}_sc_3D.h5'.format(contrast_type))
fname_seg_crop_res = sct.add_suffix(fname_res3d, '_seg')
segment_3d(model_fname=segmentation_model_fname,
contrast_type=contrast_type,
fname_in=fname_res3d,
fname_out=fname_seg_crop_res)
# resample to the initial pz resolution
fname_seg_res2d = sct.add_suffix(fname_seg_crop_res, '_resampled2d')
initial_2d_resolution = 'x'.join(
['0.5', '0.5', str(input_resolution[2])])
spinalcordtoolbox.resample.nipy_resample.resample_file(
fname_seg_crop_res,
fname_seg_res2d,
initial_2d_resolution,
'mm',
'linear',
verbose=0)
seg_crop_data = Image(fname_seg_res2d).data
# reconstruct the segmentation from the crop data
sct.log.info("Reassembling the image...")
fname_seg_res_RPI = sct.add_suffix(file_fname, '_res_RPI_seg')
uncrop_image(fname_ref=fname_res,
fname_out=fname_seg_res_RPI,
data_crop=seg_crop_data,
x_crop_lst=X_CROP_LST,
y_crop_lst=Y_CROP_LST)
# resample to initial resolution
sct.log.info(
"Resampling the segmentation to the original image resolution...")
fname_seg_RPI = sct.add_suffix(file_fname, '_RPI_seg')
initial_resolution = 'x'.join([
str(input_resolution[0]),
str(input_resolution[1]),
str(input_resolution[2])
])
spinalcordtoolbox.resample.nipy_resample.resample_file(fname_seg_res_RPI,
fname_seg_RPI,
initial_resolution,
'mm',
'linear',
verbose=0)
# binarize the resampled image to remove interpolation effects
sct.log.info(
"Binarizing the segmentation to avoid interpolation effects...")
thr = '0.0001' if contrast_type in ['t1', 'dwi'] else '0.5'
sct.run(
['sct_maths', '-i', fname_seg_RPI, '-bin', thr, '-o', fname_seg_RPI],
verbose=0)
# post processing step to z_regularized
post_processing_volume_wise(fname_in=fname_seg_RPI)
# reorient to initial orientation
sct.log.info(
"Reorienting the segmentation to the original image orientation...")
fname_seg = sct.add_suffix(file_fname, '_seg')
if original_orientation != 'RPI':
im_seg_orient = set_orientation(Image(fname_seg_RPI),
original_orientation)
im_seg_orient.setFileName(fname_seg)
im_seg_orient.save()
else:
sct.copy(fname_seg_RPI, fname_seg)
tmp_folder.chdir_undo()
# copy image from temporary folder into output folder
sct.copy(os.path.join(tmp_folder_path, fname_seg), output_folder)
# remove temporary files
if remove_temp_files:
sct.log.info("Remove temporary files...")
tmp_folder.cleanup()
return os.path.join(output_folder, fname_seg)
def main():
# 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 ' FWHM .............................. '+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')
# ## new
#
# ### Make sure that centerline file does not have halls
# file_c = load('centerline_rpi.nii')
# data_c = file_c.get_data()
# hdr_c = file_c.get_header()
#
# data_temp = copy(data_c)
# data_temp *= 0
# data_output = copy(data_c)
# data_output *= 0
# nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension('centerline_rpi.nii')
#
# ## Change seg to centerline if it is a segmentation
# sct.printv('\nChange segmentation to centerline if it is a centerline...\n')
# z_centerline = [iz for iz in range(0, nz, 1) if data_c[:,:,iz].any() ]
# nz_nonz = len(z_centerline)
# if nz_nonz==0 :
# print '\nERROR: Centerline is empty'
# sys.exit()
# x_centerline = [0 for iz in range(0, nz_nonz, 1)]
# y_centerline = [0 for iz in range(0, nz_nonz, 1)]
# #print("z_centerline", z_centerline,nz_nonz,len(x_centerline))
# print '\nGet center of mass of the centerline ...'
# for iz in xrange(len(z_centerline)):
# x_centerline[iz], y_centerline[iz] = ndimage.measurements.center_of_mass(array(data_c[:,:,z_centerline[iz]]))
# data_temp[x_centerline[iz], y_centerline[iz], z_centerline[iz]] = 1
#
# ## Complete centerline
# sct.printv('\nComplete the halls of the centerline if there are any...\n')
# X,Y,Z = data_temp.nonzero()
#
# x_centerline_extended = [0 for i in range(0, nz, 1)]
# y_centerline_extended = [0 for i in range(0, nz, 1)]
# for iz in range(len(Z)):
# x_centerline_extended[Z[iz]] = X[iz]
# y_centerline_extended[Z[iz]] = Y[iz]
#
# X_centerline_extended = nonzero(x_centerline_extended)
# X_centerline_extended = transpose(X_centerline_extended)
# Y_centerline_extended = nonzero(y_centerline_extended)
# Y_centerline_extended = transpose(Y_centerline_extended)
#
# # initialization: we set the extrem values to avoid edge effects
# x_centerline_extended[0] = x_centerline_extended[X_centerline_extended[0]]
# x_centerline_extended[-1] = x_centerline_extended[X_centerline_extended[-1]]
# y_centerline_extended[0] = y_centerline_extended[Y_centerline_extended[0]]
# y_centerline_extended[-1] = y_centerline_extended[Y_centerline_extended[-1]]
#
# # Add two rows to the vector X_means_smooth_extended:
# # one before as means_smooth_extended[0] is now diff from 0
# # one after as means_smooth_extended[-1] is now diff from 0
# X_centerline_extended = append(X_centerline_extended, len(x_centerline_extended)-1)
# X_centerline_extended = insert(X_centerline_extended, 0, 0)
# Y_centerline_extended = append(Y_centerline_extended, len(y_centerline_extended)-1)
# Y_centerline_extended = insert(Y_centerline_extended, 0, 0)
#
# #recurrence
# count_zeros_x=0
# count_zeros_y=0
# for i in range(1,nz-1):
# if x_centerline_extended[i]==0:
# x_centerline_extended[i] = 0.5*(x_centerline_extended[X_centerline_extended[i-1-count_zeros_x]] + x_centerline_extended[X_centerline_extended[i-count_zeros_x]])
# count_zeros_x += 1
# if y_centerline_extended[i]==0:
# y_centerline_extended[i] = 0.5*(y_centerline_extended[Y_centerline_extended[i-1-count_zeros_y]] + y_centerline_extended[Y_centerline_extended[i-count_zeros_y]])
# count_zeros_y += 1
#
# # Save image centerline completed to be used after
# sct.printv('\nSave image completed: centerline_rpi_completed.nii...\n')
# for i in range(nz):
# data_output[x_centerline_extended[i],y_centerline_extended[i],i] = 1
# img = Nifti1Image(data_output, None, hdr_c)
# save(img, 'centerline_rpi_completed.nii')
#
# #end new
# Straighten the spinal cord
print '\nStraighten the spinal cord...'
sct.run('sct_straighten_spinalcord -i anat_rpi.nii -s centerline_rpi.nii -qc 0 -x spline -v '+str(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)
# 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.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 process(self):
# preprocessing
os.chdir(self.tmp_dir)
im_target = Image(self.original_target)
im_sc_seg = Image(self.original_sc_seg)
self.original_header = im_target.hdr
self.original_orientation = im_target.orientation
index_x = self.original_orientation.find('R') if 'R' in self.original_orientation else self.original_orientation.find('L')
index_y = self.original_orientation.find('P') if 'P' in self.original_orientation else self.original_orientation.find('A')
index_z = self.original_orientation.find('I') if 'I' in self.original_orientation else self.original_orientation.find('S')
# resampling of the images
nx, ny, nz, nt, px, py, pz, pt = im_target.dim
pix_dim = [px, py, pz]
self.original_px = pix_dim[index_x]
self.original_py = pix_dim[index_y]
if round(self.original_px, 2) != self.resample_to or round(self.original_py, 2) != self.resample_to:
self.t2star = resample_image(self.original_target, npx=self.resample_to, npy=self.resample_to)
self.sc_seg = resample_image(self.original_sc_seg, binary=True, npx=self.resample_to, npy=self.resample_to)
# denoising (optional)
im_target = Image(self.t2star)
if self.denoising:
from sct_maths import denoise_ornlm
im_target.data = denoise_ornlm(im_target.data)
im_target.save()
self.t2star = im_target.file_name + im_target.ext
box_size = int(22.5/self.resample_to)
# Pad in case the spinal cord is too close to the edges
pad_size = box_size/2 + 2
self.pad = [str(pad_size)]*3
self.pad[index_z] = str(0)
t2star_pad = sct.add_suffix(self.t2star, '_pad')
sc_seg_pad = sct.add_suffix(self.sc_seg, '_pad')
sct.run('sct_image -i '+self.t2star+' -pad '+self.pad[0]+','+self.pad[1]+','+self.pad[2]+' -o '+t2star_pad)
sct.run('sct_image -i '+self.sc_seg+' -pad '+self.pad[0]+','+self.pad[1]+','+self.pad[2]+' -o '+sc_seg_pad)
self.t2star = t2star_pad
self.sc_seg = sc_seg_pad
# put data in RPI
t2star_rpi = sct.add_suffix(self.t2star, '_RPI')
sc_seg_rpi = sct.add_suffix(self.sc_seg, '_RPI')
sct.run('sct_image -i '+self.t2star+' -setorient RPI -o '+t2star_rpi)
sct.run('sct_image -i '+self.sc_seg+' -setorient RPI -o '+sc_seg_rpi)
self.t2star = t2star_rpi
self.sc_seg = sc_seg_rpi
self.square_mask, self.processed_target = crop_t2_star(self.t2star, self.sc_seg, box_size=box_size)
if self.t2 is not None:
self.fname_level = compute_level_file(self.t2star, self.sc_seg, self.t2, self.t2_seg, self.t2_landmarks)
elif self.fname_level is not None:
level_orientation = get_orientation(self.fname_level, filename=True)
if level_orientation != 'IRP':
self.fname_level = set_orientation(self.fname_level, 'IRP', filename=True)
os.chdir('..')
def post_processing(self):
## DO INTERPOLATION BACK TO ORIGINAL IMAGE
# get original SC segmentation oriented in RPI
im_sc_seg_original_rpi = self.info_preprocessing['im_sc_seg_rpi'].copy()
nx_ref, ny_ref, nz_ref, nt_ref, px_ref, py_ref, pz_ref, pt_ref = im_sc_seg_original_rpi.dim
# create res GM seg image
im_res_gmseg = im_sc_seg_original_rpi.copy()
im_res_gmseg.data = np.zeros(im_res_gmseg.data.shape)
# create res WM seg image
im_res_wmseg = im_sc_seg_original_rpi.copy()
im_res_wmseg.data = np.zeros(im_res_wmseg.data.shape)
printv(' Interpolate result back into original space...', self.param.verbose, 'normal')
for iz, im_iz_preprocessed in enumerate(self.info_preprocessing['interpolated_images']):
# im gmseg for slice iz
im_gmseg = im_iz_preprocessed.copy()
im_gmseg.data = np.zeros(im_gmseg.data.shape)
im_gmseg.data = self.target_im[iz].gm_seg
# im wmseg for slice iz
im_wmseg = im_iz_preprocessed.copy()
im_wmseg.data = np.zeros(im_wmseg.data.shape)
im_wmseg.data = self.target_im[iz].wm_seg
for im_res_slice, im_res_tot in [(im_gmseg, im_res_gmseg), (im_wmseg, im_res_wmseg)]:
# get reference image for this slice
# (use only one slice to accelerate interpolation)
im_ref = im_sc_seg_original_rpi.copy()
im_ref.data = im_ref.data[:, :, iz]
im_ref.dim = (nx_ref, ny_ref, 1, nt_ref, px_ref, py_ref, pz_ref, pt_ref)
# correct reference header for this slice
[[x_0_ref, y_0_ref, z_0_ref]] = im_ref.transfo_pix2phys(coordi=[[0, 0, iz]])
im_ref.hdr.as_analyze_map()['qoffset_x'] = x_0_ref
im_ref.hdr.as_analyze_map()['qoffset_y'] = y_0_ref
im_ref.hdr.as_analyze_map()['qoffset_z'] = z_0_ref
im_ref.hdr.set_sform(im_ref.hdr.get_qform())
im_ref.hdr.set_qform(im_ref.hdr.get_qform())
# set im_res_slice header with im_sc_seg_original_rpi origin
im_res_slice.hdr.as_analyze_map()['qoffset_x'] = x_0_ref
im_res_slice.hdr.as_analyze_map()['qoffset_y'] = y_0_ref
im_res_slice.hdr.as_analyze_map()['qoffset_z'] = z_0_ref
im_res_slice.hdr.set_sform(im_res_slice.hdr.get_qform())
im_res_slice.hdr.set_qform(im_res_slice.hdr.get_qform())
# get physical coordinates of center of sc
x_seg, y_seg = (im_sc_seg_original_rpi.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_sc_seg_original_rpi.transfo_pix2phys(coordi=[[x_center, y_center, iz]])
# get physical coordinates of center of square WITH im_res_slice WITH SAME ORIGIN AS im_sc_seg_original_rpi
sq_size_pix = int(self.param_data.square_size_size_mm / self.param_data.axial_res)
[[x_square_center_phys, y_square_center_phys, z_square_center_phys]] = im_res_slice.transfo_pix2phys(
coordi=[[int(sq_size_pix / 2), int(sq_size_pix / 2), 0]])
# set im_res_slice header by adding center of SC and center of square (in the correct space) to origin
im_res_slice.hdr.as_analyze_map()['qoffset_x'] += x_center_phys - x_square_center_phys
im_res_slice.hdr.as_analyze_map()['qoffset_y'] += y_center_phys - y_square_center_phys
im_res_slice.hdr.as_analyze_map()['qoffset_z'] += z_center_phys
im_res_slice.hdr.set_sform(im_res_slice.hdr.get_qform())
im_res_slice.hdr.set_qform(im_res_slice.hdr.get_qform())
# reshape data
im_res_slice.data = im_res_slice.data.reshape((sq_size_pix, sq_size_pix, 1))
# interpolate to reference image
interp = 0 if self.param_seg.type_seg == 'bin' else 1
im_res_slice_interp = im_res_slice.interpolate_from_image(im_ref, interpolation_mode=interp, border='nearest')
# set correct slice of total image with this slice
if len(im_res_slice_interp.data.shape) == 3:
shape_x, shape_y, shape_z = im_res_slice_interp.data.shape
im_res_slice_interp.data = im_res_slice_interp.data.reshape((shape_x, shape_y))
im_res_tot.data[:, :, iz] = im_res_slice_interp.data
printv(' Reorient resulting segmentations to native orientation...', self.param.verbose, 'normal')
## PUT RES BACK IN ORIGINAL ORIENTATION
im_res_gmseg.setFileName('res_gmseg.nii.gz')
im_res_gmseg.save()
im_res_gmseg = set_orientation(im_res_gmseg, self.info_preprocessing['orientation'])
im_res_wmseg.setFileName('res_wmseg.nii.gz')
im_res_wmseg.save()
im_res_wmseg = set_orientation(im_res_wmseg, self.info_preprocessing['orientation'])
return im_res_gmseg, im_res_wmseg
def main(fname_anat, fname_centerline, degree_poly, centerline_fitting, interp,
remove_temp_files, verbose):
# extract path of the script
path_script = os.path.dirname(__file__) + '/'
# Parameters for debug mode
if param.debug == 1:
print '\n*** WARNING: DEBUG MODE ON ***\n'
status, path_sct_data = commands.getstatusoutput(
'echo $SCT_TESTING_DATA_DIR')
fname_anat = path_sct_data + '/t2/t2.nii.gz'
fname_centerline = path_sct_data + '/t2/t2_seg.nii.gz'
# extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
# Display arguments
print '\nCheck input arguments...'
print ' Input volume ...................... ' + fname_anat
print ' Centerline ........................ ' + fname_centerline
print ''
# Get input image orientation
im_anat = Image(fname_anat)
input_image_orientation = get_orientation_3d(im_anat)
# Reorient input data into RL PA IS orientation
im_centerline = Image(fname_centerline)
im_anat_orient = set_orientation(im_anat, 'RPI')
im_anat_orient.setFileName('tmp.anat_orient.nii')
im_centerline_orient = set_orientation(im_centerline, 'RPI')
im_centerline_orient.setFileName('tmp.centerline_orient.nii')
# Open centerline
#==========================================================================================
print '\nGet dimensions of input centerline...'
nx, ny, nz, nt, px, py, pz, pt = im_centerline_orient.dim
print '.. matrix size: ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz)
print '.. voxel size: ' + str(px) + 'mm x ' + str(py) + 'mm x ' + str(
pz) + 'mm'
print '\nOpen centerline volume...'
data = im_centerline_orient.data
X, Y, Z = (data > 0).nonzero()
min_z_index, max_z_index = min(Z), max(Z)
# loop across z and associate x,y coordinate with the point having maximum intensity
x_centerline = [0 for iz in range(min_z_index, max_z_index + 1, 1)]
y_centerline = [0 for iz in range(min_z_index, max_z_index + 1, 1)]
z_centerline = [iz for iz in range(min_z_index, max_z_index + 1, 1)]
# Two possible scenario:
# 1. the centerline is probabilistic: each slices contains voxels with the probability of containing the centerline [0:...:1]
# We only take the maximum value of the image to aproximate the centerline.
# 2. The centerline/segmentation image contains many pixels per slice with values {0,1}.
# We take all the points and approximate the centerline on all these points.
X, Y, Z = ((data < 1) * (data > 0)).nonzero() # X is empty if binary image
if (len(X) > 0): # Scenario 1
for iz in range(min_z_index, max_z_index + 1, 1):
x_centerline[iz - min_z_index], y_centerline[
iz - min_z_index] = numpy.unravel_index(
data[:, :, iz].argmax(), data[:, :, iz].shape)
else: # Scenario 2
for iz in range(min_z_index, max_z_index + 1, 1):
x_seg, y_seg = (data[:, :, iz] > 0).nonzero()
if len(x_seg) > 0:
x_centerline[iz - min_z_index] = numpy.mean(x_seg)
y_centerline[iz - min_z_index] = numpy.mean(y_seg)
# TODO: find a way to do the previous loop with this, which is more neat:
# [numpy.unravel_index(data[:,:,iz].argmax(), data[:,:,iz].shape) for iz in range(0,nz,1)]
# clear variable
del data
# Fit the centerline points with the kind of curve given as argument of the script and return the new smoothed coordinates
if centerline_fitting == 'nurbs':
try:
x_centerline_fit, y_centerline_fit = b_spline_centerline(
x_centerline, y_centerline, z_centerline)
except ValueError:
print "splines fitting doesn't work, trying with polynomial fitting...\n"
x_centerline_fit, y_centerline_fit = polynome_centerline(
x_centerline, y_centerline, z_centerline)
elif centerline_fitting == 'polynome':
x_centerline_fit, y_centerline_fit = polynome_centerline(
x_centerline, y_centerline, z_centerline)
#==========================================================================================
# Split input volume
print '\nSplit input volume...'
im_anat_orient_split_list = split_data(im_anat_orient, 2)
file_anat_split = []
for im in im_anat_orient_split_list:
file_anat_split.append(im.absolutepath)
im.save()
# initialize variables
file_mat_inv_cumul = [
'tmp.mat_inv_cumul_Z' + str(z).zfill(4) for z in range(0, nz, 1)
]
z_init = min_z_index
displacement_max_z_index = x_centerline_fit[
z_init - min_z_index] - x_centerline_fit[max_z_index - min_z_index]
# write centerline as text file
print '\nGenerate fitted transformation matrices...'
file_mat_inv_cumul_fit = [
'tmp.mat_inv_cumul_fit_Z' + str(z).zfill(4) for z in range(0, nz, 1)
]
for iz in range(min_z_index, max_z_index + 1, 1):
# compute inverse cumulative fitted transformation matrix
fid = open(file_mat_inv_cumul_fit[iz], 'w')
if (x_centerline[iz - min_z_index] == 0
and y_centerline[iz - min_z_index] == 0):
displacement = 0
else:
displacement = x_centerline_fit[
z_init - min_z_index] - x_centerline_fit[iz - min_z_index]
fid.write('%i %i %i %f\n' % (1, 0, 0, displacement))
fid.write('%i %i %i %f\n' % (0, 1, 0, 0))
fid.write('%i %i %i %i\n' % (0, 0, 1, 0))
fid.write('%i %i %i %i\n' % (0, 0, 0, 1))
fid.close()
# we complete the displacement matrix in z direction
for iz in range(0, min_z_index, 1):
fid = open(file_mat_inv_cumul_fit[iz], 'w')
fid.write('%i %i %i %f\n' % (1, 0, 0, 0))
fid.write('%i %i %i %f\n' % (0, 1, 0, 0))
fid.write('%i %i %i %i\n' % (0, 0, 1, 0))
fid.write('%i %i %i %i\n' % (0, 0, 0, 1))
fid.close()
for iz in range(max_z_index + 1, nz, 1):
fid = open(file_mat_inv_cumul_fit[iz], 'w')
fid.write('%i %i %i %f\n' % (1, 0, 0, displacement_max_z_index))
fid.write('%i %i %i %f\n' % (0, 1, 0, 0))
fid.write('%i %i %i %i\n' % (0, 0, 1, 0))
fid.write('%i %i %i %i\n' % (0, 0, 0, 1))
fid.close()
# apply transformations to data
print '\nApply fitted transformation matrices...'
file_anat_split_fit = [
'tmp.anat_orient_fit_Z' + str(z).zfill(4) for z in range(0, nz, 1)
]
for iz in range(0, nz, 1):
# forward cumulative transformation to data
sct.run(fsloutput + 'flirt -in ' + file_anat_split[iz] + ' -ref ' +
file_anat_split[iz] + ' -applyxfm -init ' +
file_mat_inv_cumul_fit[iz] + ' -out ' +
file_anat_split_fit[iz] + ' -interp ' + interp)
# Merge into 4D volume
print '\nMerge into 4D volume...'
from glob import glob
im_to_concat_list = [
Image(fname) for fname in glob('tmp.anat_orient_fit_Z*.nii')
]
im_concat_out = concat_data(im_to_concat_list, 2)
im_concat_out.setFileName('tmp.anat_orient_fit.nii')
im_concat_out.save()
# sct.run(fsloutput+'fslmerge -z tmp.anat_orient_fit tmp.anat_orient_fit_z*')
# Reorient data as it was before
print '\nReorient data back into native orientation...'
fname_anat_fit_orient = set_orientation(im_concat_out.absolutepath,
input_image_orientation,
filename=True)
move(fname_anat_fit_orient, 'tmp.anat_orient_fit_reorient.nii')
# Generate output file (in current folder)
print '\nGenerate output file (in current folder)...'
sct.generate_output_file('tmp.anat_orient_fit_reorient.nii',
file_anat + '_flatten' + ext_anat)
# Delete temporary files
if remove_temp_files == 1:
print '\nDelete temporary files...'
sct.run('rm -rf tmp.*')
# to view results
print '\nDone! To view results, type:'
print 'fslview ' + file_anat + ext_anat + ' ' + file_anat + '_flatten' + ext_anat + ' &\n'
def main(args=None):
# Initialization
# fname_anat = ''
# fname_centerline = ''
sigma = 3 # default value of the standard deviation for the Gaussian smoothing (in terms of number of voxels)
# remove_temp_files = param.remove_temp_files
# verbose = param.verbose
start_time = time.time()
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
fname_anat = arguments['-i']
fname_centerline = arguments['-s']
if '-smooth' in arguments:
sigma = arguments['-smooth']
if '-r' in arguments:
remove_temp_files = int(arguments['-r'])
if '-v' in arguments:
verbose = int(arguments['-v'])
# Display arguments
sct.printv('\nCheck input arguments...')
sct.printv(' Volume to smooth .................. ' + fname_anat)
sct.printv(' Centerline ........................ ' + fname_centerline)
sct.printv(' Sigma (mm) ........................ ' + str(sigma))
sct.printv(' Verbose ........................... ' + str(verbose))
# Check that input is 3D:
from msct_image import Image
nx, ny, nz, nt, px, py, pz, pt = Image(fname_anat).dim
dim = 4 # by default, will be adjusted later
if nt == 1:
dim = 3
if nz == 1:
dim = 2
if dim == 4:
sct.printv(
'WARNING: the input image is 4D, please split your image to 3D before smoothing spinalcord using :\n'
'sct_image -i ' + fname_anat + ' -split t -o ' + fname_anat,
verbose, 'warning')
sct.printv('4D images not supported, aborting ...', verbose, 'error')
# Extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
path_centerline, file_centerline, ext_centerline = sct.extract_fname(
fname_centerline)
# create temporary folder
sct.printv('\nCreate temporary folder...', verbose)
path_tmp = sct.slash_at_the_end('tmp.' + time.strftime("%y%m%d%H%M%S"), 1)
sct.run('mkdir ' + path_tmp, verbose)
# Copying input data to tmp folder
sct.printv('\nCopying input data to tmp folder and convert to nii...',
verbose)
sct.run('cp ' + fname_anat + ' ' + path_tmp + 'anat' + ext_anat, verbose)
sct.run(
'cp ' + fname_centerline + ' ' + path_tmp + 'centerline' +
ext_centerline, verbose)
# go to tmp folder
os.chdir(path_tmp)
# convert to nii format
convert('anat' + ext_anat, 'anat.nii')
convert('centerline' + ext_centerline, 'centerline.nii')
# Change orientation of the input image into RPI
sct.printv('\nOrient input volume to RPI orientation...')
fname_anat_rpi = set_orientation('anat.nii', 'RPI', filename=True)
move(fname_anat_rpi, 'anat_rpi.nii')
# Change orientation of the input image into RPI
sct.printv('\nOrient centerline to RPI orientation...')
fname_centerline_rpi = set_orientation('centerline.nii',
'RPI',
filename=True)
move(fname_centerline_rpi, 'centerline_rpi.nii')
# Straighten the spinal cord
# straighten segmentation
sct.printv('\nStraighten the spinal cord using centerline/segmentation...',
verbose)
# check if warp_curve2straight and warp_straight2curve already exist (i.e. no need to do it another time)
if os.path.isfile('../warp_curve2straight.nii.gz') and os.path.isfile(
'../warp_straight2curve.nii.gz') and os.path.isfile(
'../straight_ref.nii.gz'):
# if they exist, copy them into current folder
sct.printv(
'WARNING: Straightening was already run previously. Copying warping fields...',
verbose, 'warning')
shutil.copy('../warp_curve2straight.nii.gz',
'warp_curve2straight.nii.gz')
shutil.copy('../warp_straight2curve.nii.gz',
'warp_straight2curve.nii.gz')
shutil.copy('../straight_ref.nii.gz', 'straight_ref.nii.gz')
# apply straightening
sct.run(
'sct_apply_transfo -i anat_rpi.nii -w warp_curve2straight.nii.gz -d straight_ref.nii.gz -o anat_rpi_straight.nii -x spline',
verbose)
else:
sct.run(
'sct_straighten_spinalcord -i anat_rpi.nii -s centerline_rpi.nii -qc 0 -x spline',
verbose)
# Smooth the straightened image along z
sct.printv('\nSmooth the straightened image along z...')
sct.run(
'sct_maths -i anat_rpi_straight.nii -smooth 0,0,' + str(sigma) +
' -o anat_rpi_straight_smooth.nii', verbose)
# Apply the reversed warping field to get back the curved spinal cord
sct.printv(
'\nApply the reversed warping field to get back the curved spinal cord...'
)
sct.run(
'sct_apply_transfo -i anat_rpi_straight_smooth.nii -o anat_rpi_straight_smooth_curved.nii -d anat.nii -w warp_straight2curve.nii.gz -x spline',
verbose)
# replace zeroed voxels by original image (issue #937)
sct.printv('\nReplace zeroed voxels by original image...', verbose)
nii_smooth = Image('anat_rpi_straight_smooth_curved.nii')
data_smooth = nii_smooth.data
data_input = Image('anat.nii').data
indzero = np.where(data_smooth == 0)
data_smooth[indzero] = data_input[indzero]
nii_smooth.data = data_smooth
nii_smooth.setFileName('anat_rpi_straight_smooth_curved_nonzero.nii')
nii_smooth.save()
# come back to parent folder
os.chdir('..')
# Generate output file
sct.printv('\nGenerate output file...')
sct.generate_output_file(
path_tmp + '/anat_rpi_straight_smooth_curved_nonzero.nii',
file_anat + '_smooth' + ext_anat)
# Remove temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...')
sct.run('rm -rf ' + path_tmp)
# Display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(round(elapsed_time))) +
's\n')
# to view results
sct.printv('Done! To view results, type:', verbose)
sct.printv('fslview ' + file_anat + ' ' + file_anat + '_smooth &\n',
verbose, 'info')
def get_centerline_from_point(input_image, point_file, gap=4, gaussian_kernel=4, remove_tmp_files=1):
# Initialization
fname_anat = input_image
fname_point = point_file
slice_gap = gap
remove_tmp_files = remove_tmp_files
gaussian_kernel = gaussian_kernel
start_time = time()
verbose = 1
# get path of the toolbox
status, path_sct = commands.getstatusoutput("echo $SCT_DIR")
path_sct = sct.slash_at_the_end(path_sct, 1)
# Parameters for debug mode
if param.debug == 1:
sct.printv("\n*** WARNING: DEBUG MODE ON ***\n\t\t\tCurrent working directory: " + os.getcwd(), "warning")
status, path_sct_testing_data = commands.getstatusoutput("echo $SCT_TESTING_DATA_DIR")
fname_anat = path_sct_testing_data + "/t2/t2.nii.gz"
fname_point = path_sct_testing_data + "/t2/t2_centerline_init.nii.gz"
slice_gap = 5
# check existence of input files
sct.check_file_exist(fname_anat)
sct.check_file_exist(fname_point)
# extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
path_point, file_point, ext_point = sct.extract_fname(fname_point)
# extract path of schedule file
# TODO: include schedule file in sct
# TODO: check existence of schedule file
file_schedule = path_sct + param.schedule_file
# Get input image orientation
input_image_orientation = get_orientation_3d(fname_anat, filename=True)
# Display arguments
print "\nCheck input arguments..."
print " Anatomical image: " + fname_anat
print " Orientation: " + input_image_orientation
print " Point in spinal cord: " + fname_point
print " Slice gap: " + str(slice_gap)
print " Gaussian kernel: " + str(gaussian_kernel)
print " Degree of polynomial: " + str(param.deg_poly)
# create temporary folder
print ("\nCreate temporary folder...")
path_tmp = "tmp." + strftime("%y%m%d%H%M%S")
sct.create_folder(path_tmp)
print "\nCopy input data..."
sct.run("cp " + fname_anat + " " + path_tmp + "/tmp.anat" + ext_anat)
sct.run("cp " + fname_point + " " + path_tmp + "/tmp.point" + ext_point)
# go to temporary folder
os.chdir(path_tmp)
# convert to nii
im_anat = convert("tmp.anat" + ext_anat, "tmp.anat.nii")
im_point = convert("tmp.point" + ext_point, "tmp.point.nii")
# Reorient input anatomical volume into RL PA IS orientation
print "\nReorient input volume to RL PA IS orientation..."
set_orientation(im_anat, "RPI")
im_anat.setFileName("tmp.anat_orient.nii")
# Reorient binary point into RL PA IS orientation
print "\nReorient binary point into RL PA IS orientation..."
# sct.run(sct.fsloutput + 'fslswapdim tmp.point RL PA IS tmp.point_orient')
set_orientation(im_point, "RPI")
im_point.setFileName("tmp.point_orient.nii")
# Get image dimensions
print "\nGet image dimensions..."
nx, ny, nz, nt, px, py, pz, pt = Image("tmp.anat_orient.nii").dim
print ".. matrix size: " + str(nx) + " x " + str(ny) + " x " + str(nz)
print ".. voxel size: " + str(px) + "mm x " + str(py) + "mm x " + str(pz) + "mm"
# Split input volume
print "\nSplit input volume..."
im_anat_split_list = split_data(im_anat, 2)
file_anat_split = []
for im in im_anat_split_list:
file_anat_split.append(im.absolutepath)
im.save()
im_point_split_list = split_data(im_point, 2)
file_point_split = []
for im in im_point_split_list:
file_point_split.append(im.absolutepath)
im.save()
# Extract coordinates of input point
data_point = Image("tmp.point_orient.nii").data
x_init, y_init, z_init = unravel_index(data_point.argmax(), data_point.shape)
sct.printv("Coordinates of input point: (" + str(x_init) + ", " + str(y_init) + ", " + str(z_init) + ")", verbose)
# Create 2D gaussian mask
sct.printv("\nCreate gaussian mask from point...", verbose)
xx, yy = mgrid[:nx, :ny]
mask2d = zeros((nx, ny))
radius = round(float(gaussian_kernel + 1) / 2) # add 1 because the radius includes the center.
sigma = float(radius)
mask2d = exp(-(((xx - x_init) ** 2) / (2 * (sigma ** 2)) + ((yy - y_init) ** 2) / (2 * (sigma ** 2))))
# Save mask to 2d file
file_mask_split = ["tmp.mask_orient_Z" + str(z).zfill(4) for z in range(0, nz, 1)]
nii_mask2d = Image("tmp.anat_orient_Z0000.nii")
nii_mask2d.data = mask2d
nii_mask2d.setFileName(file_mask_split[z_init] + ".nii")
nii_mask2d.save()
# initialize variables
file_mat = ["tmp.mat_Z" + str(z).zfill(4) for z in range(0, nz, 1)]
file_mat_inv = ["tmp.mat_inv_Z" + str(z).zfill(4) for z in range(0, nz, 1)]
file_mat_inv_cumul = ["tmp.mat_inv_cumul_Z" + str(z).zfill(4) for z in range(0, nz, 1)]
# create identity matrix for initial transformation matrix
fid = open(file_mat_inv_cumul[z_init], "w")
fid.write("%i %i %i %i\n" % (1, 0, 0, 0))
fid.write("%i %i %i %i\n" % (0, 1, 0, 0))
fid.write("%i %i %i %i\n" % (0, 0, 1, 0))
fid.write("%i %i %i %i\n" % (0, 0, 0, 1))
fid.close()
# initialize centerline: give value corresponding to initial point
x_centerline = [x_init]
y_centerline = [y_init]
z_centerline = [z_init]
warning_count = 0
# go up (1), then down (2) in reference to the binary point
for iUpDown in range(1, 3):
if iUpDown == 1:
# z increases
slice_gap_signed = slice_gap
elif iUpDown == 2:
# z decreases
slice_gap_signed = -slice_gap
# reverse centerline (because values will be appended at the end)
x_centerline.reverse()
y_centerline.reverse()
z_centerline.reverse()
# initialization before looping
z_dest = z_init # point given by user
z_src = z_dest + slice_gap_signed
# continue looping if 0 <= z < nz
while 0 <= z_src < nz:
# print current z:
print "z=" + str(z_src) + ":"
# estimate transformation
sct.run(
fsloutput
+ "flirt -in "
+ file_anat_split[z_src]
+ " -ref "
+ file_anat_split[z_dest]
+ " -schedule "
+ file_schedule
+ " -verbose 0 -omat "
+ file_mat[z_src]
+ " -cost normcorr -forcescaling -inweight "
+ file_mask_split[z_dest]
+ " -refweight "
+ file_mask_split[z_dest]
)
# display transfo
status, output = sct.run("cat " + file_mat[z_src])
print output
# check if transformation is bigger than 1.5x slice_gap
tx = float(output.split()[3])
ty = float(output.split()[7])
norm_txy = linalg.norm([tx, ty], ord=2)
if norm_txy > 1.5 * slice_gap:
print "WARNING: Transformation is too large --> using previous one."
warning_count = warning_count + 1
# if previous transformation exists, replace current one with previous one
if os.path.isfile(file_mat[z_dest]):
sct.run("cp " + file_mat[z_dest] + " " + file_mat[z_src])
# estimate inverse transformation matrix
sct.run("convert_xfm -omat " + file_mat_inv[z_src] + " -inverse " + file_mat[z_src])
# compute cumulative transformation
sct.run(
"convert_xfm -omat "
+ file_mat_inv_cumul[z_src]
+ " -concat "
+ file_mat_inv[z_src]
+ " "
+ file_mat_inv_cumul[z_dest]
)
# apply inverse cumulative transformation to initial gaussian mask (to put it in src space)
sct.run(
fsloutput
+ "flirt -in "
+ file_mask_split[z_init]
+ " -ref "
+ file_mask_split[z_init]
+ " -applyxfm -init "
+ file_mat_inv_cumul[z_src]
+ " -out "
+ file_mask_split[z_src]
)
# open inverse cumulative transformation file and generate centerline
fid = open(file_mat_inv_cumul[z_src])
mat = fid.read().split()
x_centerline.append(x_init + float(mat[3]))
y_centerline.append(y_init + float(mat[7]))
z_centerline.append(z_src)
# z_index = z_index+1
# define new z_dest (target slice) and new z_src (moving slice)
z_dest = z_dest + slice_gap_signed
z_src = z_src + slice_gap_signed
# Reconstruct centerline
# ====================================================================================================
# reverse back centerline (because it's been reversed once, so now all values are in the right order)
x_centerline.reverse()
y_centerline.reverse()
z_centerline.reverse()
# fit centerline in the Z-X plane using polynomial function
print "\nFit centerline in the Z-X plane using polynomial function..."
coeffsx = polyfit(z_centerline, x_centerline, deg=param.deg_poly)
polyx = poly1d(coeffsx)
x_centerline_fit = polyval(polyx, z_centerline)
# calculate RMSE
rmse = linalg.norm(x_centerline_fit - x_centerline) / sqrt(len(x_centerline))
# calculate max absolute error
max_abs = max(abs(x_centerline_fit - x_centerline))
print ".. RMSE (in mm): " + str(rmse * px)
print ".. Maximum absolute error (in mm): " + str(max_abs * px)
# fit centerline in the Z-Y plane using polynomial function
print "\nFit centerline in the Z-Y plane using polynomial function..."
coeffsy = polyfit(z_centerline, y_centerline, deg=param.deg_poly)
polyy = poly1d(coeffsy)
y_centerline_fit = polyval(polyy, z_centerline)
# calculate RMSE
rmse = linalg.norm(y_centerline_fit - y_centerline) / sqrt(len(y_centerline))
# calculate max absolute error
max_abs = max(abs(y_centerline_fit - y_centerline))
print ".. RMSE (in mm): " + str(rmse * py)
print ".. Maximum absolute error (in mm): " + str(max_abs * py)
# display
if param.debug == 1:
import matplotlib.pyplot as plt
plt.figure()
plt.plot(z_centerline, x_centerline, ".", z_centerline, x_centerline_fit, "r")
plt.legend(["Data", "Polynomial Fit"])
plt.title("Z-X plane polynomial interpolation")
plt.show()
plt.figure()
plt.plot(z_centerline, y_centerline, ".", z_centerline, y_centerline_fit, "r")
plt.legend(["Data", "Polynomial Fit"])
plt.title("Z-Y plane polynomial interpolation")
plt.show()
# generate full range z-values for centerline
z_centerline_full = [iz for iz in range(0, nz, 1)]
# calculate X and Y values for the full centerline
x_centerline_fit_full = polyval(polyx, z_centerline_full)
y_centerline_fit_full = polyval(polyy, z_centerline_full)
# Generate fitted transformation matrices and write centerline coordinates in text file
print "\nGenerate fitted transformation matrices and write centerline coordinates in text file..."
file_mat_inv_cumul_fit = ["tmp.mat_inv_cumul_fit_z" + str(z).zfill(4) for z in range(0, nz, 1)]
file_mat_cumul_fit = ["tmp.mat_cumul_fit_z" + str(z).zfill(4) for z in range(0, nz, 1)]
fid_centerline = open("tmp.centerline_coordinates.txt", "w")
for iz in range(0, nz, 1):
# compute inverse cumulative fitted transformation matrix
fid = open(file_mat_inv_cumul_fit[iz], "w")
fid.write("%i %i %i %f\n" % (1, 0, 0, x_centerline_fit_full[iz] - x_init))
fid.write("%i %i %i %f\n" % (0, 1, 0, y_centerline_fit_full[iz] - y_init))
fid.write("%i %i %i %i\n" % (0, 0, 1, 0))
fid.write("%i %i %i %i\n" % (0, 0, 0, 1))
fid.close()
# compute forward cumulative fitted transformation matrix
sct.run("convert_xfm -omat " + file_mat_cumul_fit[iz] + " -inverse " + file_mat_inv_cumul_fit[iz])
# write centerline coordinates in x, y, z format
fid_centerline.write(
"%f %f %f\n" % (x_centerline_fit_full[iz], y_centerline_fit_full[iz], z_centerline_full[iz])
)
fid_centerline.close()
# Prepare output data
# ====================================================================================================
# write centerline as text file
for iz in range(0, nz, 1):
# compute inverse cumulative fitted transformation matrix
fid = open(file_mat_inv_cumul_fit[iz], "w")
fid.write("%i %i %i %f\n" % (1, 0, 0, x_centerline_fit_full[iz] - x_init))
fid.write("%i %i %i %f\n" % (0, 1, 0, y_centerline_fit_full[iz] - y_init))
fid.write("%i %i %i %i\n" % (0, 0, 1, 0))
fid.write("%i %i %i %i\n" % (0, 0, 0, 1))
fid.close()
# write polynomial coefficients
savetxt("tmp.centerline_polycoeffs_x.txt", coeffsx)
savetxt("tmp.centerline_polycoeffs_y.txt", coeffsy)
# apply transformations to data
print "\nApply fitted transformation matrices..."
file_anat_split_fit = ["tmp.anat_orient_fit_z" + str(z).zfill(4) for z in range(0, nz, 1)]
file_mask_split_fit = ["tmp.mask_orient_fit_z" + str(z).zfill(4) for z in range(0, nz, 1)]
file_point_split_fit = ["tmp.point_orient_fit_z" + str(z).zfill(4) for z in range(0, nz, 1)]
for iz in range(0, nz, 1):
# forward cumulative transformation to data
sct.run(
fsloutput
+ "flirt -in "
+ file_anat_split[iz]
+ " -ref "
+ file_anat_split[iz]
+ " -applyxfm -init "
+ file_mat_cumul_fit[iz]
+ " -out "
+ file_anat_split_fit[iz]
)
# inverse cumulative transformation to mask
sct.run(
fsloutput
+ "flirt -in "
+ file_mask_split[z_init]
+ " -ref "
+ file_mask_split[z_init]
+ " -applyxfm -init "
+ file_mat_inv_cumul_fit[iz]
+ " -out "
+ file_mask_split_fit[iz]
)
# inverse cumulative transformation to point
sct.run(
fsloutput
+ "flirt -in "
+ file_point_split[z_init]
+ " -ref "
+ file_point_split[z_init]
+ " -applyxfm -init "
+ file_mat_inv_cumul_fit[iz]
+ " -out "
+ file_point_split_fit[iz]
+ " -interp nearestneighbour"
)
# Merge into 4D volume
print "\nMerge into 4D volume..."
# im_anat_list = [Image(fname) for fname in glob.glob('tmp.anat_orient_fit_z*.nii')]
fname_anat_list = glob.glob("tmp.anat_orient_fit_z*.nii")
im_anat_concat = concat_data(fname_anat_list, 2)
im_anat_concat.setFileName("tmp.anat_orient_fit.nii")
im_anat_concat.save()
# im_mask_list = [Image(fname) for fname in glob.glob('tmp.mask_orient_fit_z*.nii')]
fname_mask_list = glob.glob("tmp.mask_orient_fit_z*.nii")
im_mask_concat = concat_data(fname_mask_list, 2)
im_mask_concat.setFileName("tmp.mask_orient_fit.nii")
im_mask_concat.save()
# im_point_list = [Image(fname) for fname in glob.glob('tmp.point_orient_fit_z*.nii')]
fname_point_list = glob.glob("tmp.point_orient_fit_z*.nii")
im_point_concat = concat_data(fname_point_list, 2)
im_point_concat.setFileName("tmp.point_orient_fit.nii")
im_point_concat.save()
# Copy header geometry from input data
print "\nCopy header geometry from input data..."
im_anat = Image("tmp.anat_orient.nii")
im_anat_orient_fit = Image("tmp.anat_orient_fit.nii")
im_mask_orient_fit = Image("tmp.mask_orient_fit.nii")
im_point_orient_fit = Image("tmp.point_orient_fit.nii")
im_anat_orient_fit = copy_header(im_anat, im_anat_orient_fit)
im_mask_orient_fit = copy_header(im_anat, im_mask_orient_fit)
im_point_orient_fit = copy_header(im_anat, im_point_orient_fit)
for im in [im_anat_orient_fit, im_mask_orient_fit, im_point_orient_fit]:
im.save()
# Reorient outputs into the initial orientation of the input image
print "\nReorient the centerline into the initial orientation of the input image..."
set_orientation("tmp.point_orient_fit.nii", input_image_orientation, "tmp.point_orient_fit.nii")
set_orientation("tmp.mask_orient_fit.nii", input_image_orientation, "tmp.mask_orient_fit.nii")
# Generate output file (in current folder)
print "\nGenerate output file (in current folder)..."
os.chdir("..") # come back to parent folder
fname_output_centerline = sct.generate_output_file(
path_tmp + "/tmp.point_orient_fit.nii", file_anat + "_centerline" + ext_anat
)
# Delete temporary files
if remove_tmp_files == 1:
print "\nRemove temporary files..."
sct.run("rm -rf " + path_tmp, error_exit="warning")
# print number of warnings
print "\nNumber of warnings: " + str(
warning_count
) + " (if >10, you should probably reduce the gap and/or increase the kernel size"
# display elapsed time
elapsed_time = time() - start_time
print "\nFinished! \n\tGenerated file: " + fname_output_centerline + "\n\tElapsed time: " + str(
int(round(elapsed_time))
) + "s\n"
