def test(data_path):
# parameters
folder_data = [ 'template/template/', 't2/', 'dmri/']
file_data = [get_file_label(data_path+'template/template/','T2-weighted'),
't2.nii.gz',
'warp_template2anat.nii.gz',
'dmri.nii.gz']
output = ''
status = 0
# test function
cmd = 'sct_apply_transfo -i ' + data_path + folder_data[0] + file_data[0] \
+ ' -d ' + data_path + folder_data[1] + file_data[1] \
+ ' -w ' + data_path + folder_data[1] + file_data[2]
output += cmd+'\n' # copy command
s, o = commands.getstatusoutput(cmd)
status += s
output += o
# test with 4d input
cmd = 'sct_apply_transfo -i ' + data_path + folder_data[2] + file_data[3] \
+ ' -d ' + data_path + folder_data[1] + file_data[1] \
+ ' -w ' + data_path + folder_data[1] + file_data[2]
output += cmd+'\n' # copy command
s, o = commands.getstatusoutput(cmd)
status += s
output += o
# return
#return sct.run(cmd, 0)
return status, output
def test(data_path):
# parameters
folder_data = ['template/template/', 't2/', 'dmri/']
file_data = [
get_file_label(data_path + 'template/template/', 'T2-weighted'),
't2.nii.gz', 'warp_template2anat.nii.gz', 'dmri.nii.gz'
]
output = ''
status = 0
# test function
cmd = 'sct_apply_transfo -i ' + data_path + folder_data[0] + file_data[0] \
+ ' -d ' + data_path + folder_data[1] + file_data[1] \
+ ' -w ' + data_path + folder_data[1] + file_data[2]
output += cmd + '\n' # copy command
s, o = commands.getstatusoutput(cmd)
status += s
output += o
# test with 4d input
cmd = 'sct_apply_transfo -i ' + data_path + folder_data[2] + file_data[3] \
+ ' -d ' + data_path + folder_data[1] + file_data[1] \
+ ' -w ' + data_path + folder_data[1] + file_data[2]
output += cmd + '\n' # copy command
s, o = commands.getstatusoutput(cmd)
status += s
output += o
# return
#return sct.run(cmd, 0)
return status, output
def test(data_path):
# parameters
folder_data = ['t2/', 'mt/', 'template/template/']
file_data = ['warp_template2anat.nii.gz',
'warp_t22mt1.nii.gz',
get_file_label(data_path+'template/template/', 'T2-weighted')]
# define command
cmd = 'sct_concat_transfo -w ' + data_path + folder_data[0] + file_data[0] + ',' \
+ data_path + folder_data[1] + file_data[1]\
+ ' -d ' + data_path + folder_data[2] + file_data[2]
# return
#return sct.run(cmd, 0)
return commands.getstatusoutput(cmd)
def vertebral_detection(fname, fname_seg, contrast, param, init_disc=[], verbose=1, path_template='', initc2='auto', path_output='../'):
"""
Find intervertebral discs in straightened image using template matching
:param fname:
:param fname_seg:
:param contrast:
:param param: advanced parameters
:param init_disc:
:param verbose:
:param path_template:
:param path_output: output path for verbose=2 pictures
:return:
"""
printv('\nLook for template...', verbose)
# if path_template == '':
# # get path of SCT
# from os import path
# path_script = path.dirname(__file__)
# path_sct = slash_at_the_end(path.dirname(path_script), 1)
# folder_template = 'data/template/'
# path_template = path_sct+folder_template
printv('Path template: '+path_template, verbose)
# adjust file names if MNI-Poly-AMU template is used
fname_level = get_file_label(path_template+'template/', 'vertebral', output='filewithpath')
fname_template = get_file_label(path_template+'template/', contrast.upper()+'-weighted', output='filewithpath')
# if not len(glob(path_template+'MNI-Poly-AMU*.*')) == 0:
# contrast = contrast.upper()
# file_level = '*_level.nii.gz'
# else:
# file_level = '*_levels.nii.gz'
#
# # retrieve file_template based on contrast
# try:
# fname_template_list = glob(path_template + '*' + contrast + '.nii.gz')
# fname_template = fname_template_list[0]
# except IndexError:
# printv('\nERROR: No template found. Please check the provided path.', 1, 'error')
# retrieve disc level from template
# try:
# fname_level_list = glob(path_template+file_level)
# fname_level = fname_level_list[0]
# except IndexError:
# printv('\nERROR: File *_levels.nii.gz not found.', 1, 'error')
# Open template and vertebral levels
printv('\nOpen template and vertebral levels...', verbose)
data_template = Image(fname_template).data
data_disc_template = Image(fname_level).data
# open anatomical volume
im_input = Image(fname)
data = im_input.data
# smooth data
from scipy.ndimage.filters import gaussian_filter
data = gaussian_filter(data, param.smooth_factor, output=None, mode="reflect")
# get dimension of src
nx, ny, nz = data.shape
# define xc and yc (centered in the field of view)
xc = int(round(nx/2)) # direction RL
yc = int(round(ny/2)) # direction AP
# get dimension of template
nxt, nyt, nzt = data_template.shape
# define xc and yc (centered in the field of view)
xct = int(round(nxt/2)) # direction RL
yct = int(round(nyt/2)) # direction AP
# define mean distance (in voxel) between adjacent discs: [C1/C2 -> C2/C3], [C2/C3 -> C4/C5], ..., [L1/L2 -> L2/L3]
centerline_level = data_disc_template[xct, yct, :]
# attribute value to each disc. Starts from max level, then decrease.
min_level = centerline_level[centerline_level.nonzero()].min()
max_level = centerline_level[centerline_level.nonzero()].max()
list_disc_value_template = range(min_level, max_level)
# add disc above top one
list_disc_value_template.insert(int(0), min_level - 1)
printv('\nDisc values from template: ' + str(list_disc_value_template), verbose)
# get diff to find transitions (i.e., discs)
diff_centerline_level = np.diff(centerline_level)
# get disc z-values
list_disc_z_template = diff_centerline_level.nonzero()[0].tolist()
list_disc_z_template.reverse()
printv('Z-values for each disc: ' + str(list_disc_z_template), verbose)
list_distance_template = (
np.diff(list_disc_z_template) * (-1)).tolist() # multiplies by -1 to get positive distances
printv('Distances between discs (in voxel): ' + str(list_distance_template), verbose)
# if automatic mode, find C2/C3 disc
if init_disc == [] and initc2 == 'auto':
printv('\nDetect C2/C3 disk...', verbose)
zrange = range(0, nz)
ind_c2 = list_disc_value_template.index(2)
z_peak = compute_corr_3d(src=data, target=data_template, x=xc, xshift=0, xsize=param.size_RL_initc2, y=yc, yshift=param.shift_AP_initc2, ysize=param.size_AP_initc2, z=0, zshift=param.shift_IS_initc2, zsize=param.size_IS_initc2, xtarget=xct, ytarget=yct, ztarget=list_disc_z_template[ind_c2], zrange=zrange, verbose=verbose, save_suffix='_initC2', gaussian_weighting=True, path_output=path_output)
init_disc = [z_peak, 2]
# if manual mode, open viewer for user to click on C2/C3 disc
if init_disc == [] and initc2 == 'manual':
from sct_viewer import ClickViewer
# reorient image to SAL to be compatible with viewer
im_input_SAL = im_input.copy()
im_input_SAL.change_orientation('SAL')
viewer = ClickViewer(im_input_SAL, orientation_subplot=['sag', 'ax'])
viewer.number_of_slices = 1
pz = 1
viewer.gap_inter_slice = int(10 / pz)
viewer.calculate_list_slices()
viewer.help_url = 'https://sourceforge.net/p/spinalcordtoolbox/wiki/sct_label_vertebrae/attachment/label_vertebrae_viewer.png'
# start the viewer that ask the user to enter a few points along the spinal cord
mask_points = viewer.start()
if mask_points:
# create the mask containing either the three-points or centerline mask for initialization
mask_filename = sct.add_suffix(fname, "_mask_viewer")
sct.run("sct_label_utils -i " + fname + " -create " + mask_points + " -o " + mask_filename, verbose=False)
else:
sct.printv('\nERROR: the viewer has been closed before entering all manual points. Please try again.', verbose, type='error')
# assign new init_disc_z value, which corresponds to the first vector of mask_points. Note, we need to substract from nz due to SAL orientation: in the viewer, orientation is S-I while in this code, it is I-S.
init_disc = [nz-int(mask_points.split(',')[0]), 2]
# display init disc
if verbose == 2:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.matshow(np.mean(data[xc-param.size_RL:xc+param.size_RL, :, :], axis=0).transpose(), fignum=50, cmap=plt.cm.gray, clim=[0, 800], origin='lower')
plt.title('Anatomical image')
plt.autoscale(enable=False) # to prevent autoscale of axis when displaying plot
plt.figure(50), plt.scatter(yc + param.shift_AP_visu, init_disc[0], c='yellow', s=50)
plt.text(yc + param.shift_AP_visu + 4, init_disc[0], str(init_disc[1]) + '/' + str(init_disc[1] + 1),
verticalalignment='center', horizontalalignment='left', color='pink', fontsize=15), plt.draw()
# plt.ion() # enables interactive mode
# FIND DISCS
# ===========================================================================
printv('\nDetect intervertebral discs...', verbose)
# assign initial z and disc
current_z = init_disc[0]
current_disc = init_disc[1]
# mean_distance = mean_distance * pz
# mean_distance_real = np.zeros(len(mean_distance))
# create list for z and disc
list_disc_z = []
list_disc_value = []
zrange = range(-10, 10)
direction = 'superior'
search_next_disc = True
while search_next_disc:
printv('Current disc: '+str(current_disc)+' (z='+str(current_z)+'). Direction: '+direction, verbose)
try:
# get z corresponding to current disc on template
current_z_template = list_disc_z_template[current_disc]
except:
# in case reached the bottom (see issue #849)
printv('WARNING: Reached the bottom of the template. Stop searching.', verbose, 'warning')
break
# find next disc
# N.B. Do not search for C1/C2 disc (because poorly visible), use template distance instead
if not current_disc in [1]:
current_z = compute_corr_3d(src=data, target=data_template, x=xc, xshift=0, xsize=param.size_RL, y=yc, yshift=param.shift_AP, ysize=param.size_AP, z=current_z, zshift=0, zsize=param.size_IS, xtarget=xct, ytarget=yct, ztarget=current_z_template, zrange=zrange, verbose=verbose, save_suffix='_disc'+str(current_disc), gaussian_weighting=False, path_output=path_output)
# display new disc
if verbose == 2:
plt.figure(50), plt.scatter(yc+param.shift_AP_visu, current_z, c='yellow', s=50)
plt.text(yc + param.shift_AP_visu + 4, current_z, str(current_disc)+'/'+str(current_disc+1), verticalalignment='center', horizontalalignment='left', color='yellow', fontsize=15), plt.draw()
# append to main list
if direction == 'superior':
# append at the beginning
list_disc_z.insert(0, current_z)
list_disc_value.insert(0, current_disc)
elif direction == 'inferior':
# append at the end
list_disc_z.append(current_z)
list_disc_value.append(current_disc)
# adjust correcting factor based on already-identified discs
if len(list_disc_z) > 1:
# compute distance between already-identified discs
list_distance_current = (np.diff(list_disc_z) * (-1)).tolist()
# retrieve the template distance corresponding to the already-identified discs
index_disc_identified = [i for i, j in enumerate(list_disc_value_template) if j in list_disc_value[:-1]]
list_distance_template_identified = [list_distance_template[i] for i in index_disc_identified]
# divide subject and template distances for the identified discs
list_subject_to_template_distance = [float(list_distance_current[i]) / list_distance_template_identified[i] for i in range(len(list_distance_current))]
# average across identified discs to obtain an average correcting factor
correcting_factor = np.mean(list_subject_to_template_distance)
printv('.. correcting factor: '+str(correcting_factor), verbose)
else:
correcting_factor = 1
# update list_distance specific for the subject
list_distance = [int(round(list_distance_template[i] * correcting_factor)) for i in range(len(list_distance_template))]
# updated average_disc_distance (in case it is needed)
# average_disc_distance = int(round(np.mean(list_distance)))
# assign new current_z and disc value
if direction == 'superior':
try:
approx_distance_to_next_disc = list_distance[list_disc_value_template.index(current_disc-1)]
except ValueError:
printv('WARNING: Disc value not included in template. Using previously-calculated distance: '+str(approx_distance_to_next_disc))
# assign new current_z and disc value
current_z = current_z + approx_distance_to_next_disc
current_disc = current_disc - 1
elif direction == 'inferior':
try:
approx_distance_to_next_disc = list_distance[list_disc_value_template.index(current_disc)]
except:
printv('WARNING: Disc value not included in template. Using previously-calculated distance: '+str(approx_distance_to_next_disc))
# assign new current_z and disc value
current_z = current_z - approx_distance_to_next_disc
current_disc = current_disc + 1
# if current_z is larger than searching zone, switch direction (and start from initial z minus approximate distance from updated template distance)
if current_z >= nz or current_disc == 0:
printv('.. Switching to inferior direction.', verbose)
direction = 'inferior'
current_disc = init_disc[1] + 1
current_z = init_disc[0] - list_distance[list_disc_value_template.index(current_disc)]
# if current_z is lower than searching zone, stop searching
if current_z <= 0:
search_next_disc = False
# if verbose == 2:
# # close figures
# plt.figure(fig_corr), plt.close()
# plt.figure(fig_pattern), plt.close()
# if upper disc is not 1, add disc above top disc based on mean_distance_adjusted
upper_disc = min(list_disc_value)
# if not upper_disc == 1:
printv('Adding top disc based on adjusted template distance: #'+str(upper_disc-1), verbose)
approx_distance_to_next_disc = list_distance[list_disc_value_template.index(upper_disc-1)]
next_z = max(list_disc_z) + approx_distance_to_next_disc
printv('.. approximate distance: '+str(approx_distance_to_next_disc), verbose)
# make sure next disc does not go beyond FOV in superior direction
if next_z > nz:
list_disc_z.insert(0, nz)
else:
list_disc_z.insert(0, next_z)
# assign disc value
list_disc_value.insert(0, upper_disc-1)
# Label segmentation
label_segmentation(fname_seg, list_disc_z, list_disc_value, verbose=verbose)
# save figure
if verbose == 2:
plt.figure(50), plt.savefig(path_output + 'fig_anat_straight_with_labels.png')
def main():
parser = get_parser()
param = Param()
args = sys.argv[1:]
arguments = parser.parse(args)
# get arguments
fname_data = arguments['-i']
fname_seg = arguments['-s']
fname_landmarks = arguments['-l']
if '-ofolder' in arguments:
path_output = arguments['-ofolder']
else:
path_output = ''
path_template = sct.slash_at_the_end(arguments['-t'], 1)
contrast_template = arguments['-c']
ref = arguments['-ref']
remove_temp_files = int(arguments['-r'])
verbose = int(arguments['-v'])
param.verbose = verbose # TODO: not clean, unify verbose or param.verbose in code, but not both
if '-param-straighten' in arguments:
param.param_straighten = arguments['-param-straighten']
# if '-cpu-nb' in arguments:
# arg_cpu = ' -cpu-nb '+str(arguments['-cpu-nb'])
# else:
# arg_cpu = ''
# registration parameters
if '-param' in arguments:
# reset parameters but keep step=0 (might be overwritten if user specified step=0)
paramreg = ParamregMultiStep([step0])
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# add user parameters
for paramStep in arguments['-param']:
paramreg.addStep(paramStep)
else:
paramreg = ParamregMultiStep([step0, step1, step2])
# if ref=subject, initialize registration using different affine parameters
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# initialize other parameters
# file_template_label = param.file_template_label
zsubsample = param.zsubsample
# smoothing_sigma = param.smoothing_sigma
# retrieve template file names
from sct_warp_template import get_file_label
file_template_vertebral_labeling = get_file_label(path_template + 'template/', 'vertebral')
file_template = get_file_label(path_template + 'template/', contrast_template.upper() + '-weighted')
file_template_seg = get_file_label(path_template + 'template/', 'spinal cord')
# start timer
start_time = time.time()
# get fname of the template + template objects
fname_template = path_template + 'template/' + file_template
fname_template_vertebral_labeling = path_template + 'template/' + file_template_vertebral_labeling
fname_template_seg = path_template + 'template/' + file_template_seg
# check file existence
# TODO: no need to do that!
sct.printv('\nCheck template files...')
sct.check_file_exist(fname_template, verbose)
sct.check_file_exist(fname_template_vertebral_labeling, verbose)
sct.check_file_exist(fname_template_seg, verbose)
path_data, file_data, ext_data = sct.extract_fname(fname_data)
# print arguments
sct.printv('\nCheck parameters:', verbose)
sct.printv(' Data: ' + fname_data, verbose)
sct.printv(' Landmarks: ' + fname_landmarks, verbose)
sct.printv(' Segmentation: ' + fname_seg, verbose)
sct.printv(' Path template: ' + path_template, verbose)
sct.printv(' Remove temp files: ' + str(remove_temp_files), verbose)
# create QC folder
sct.create_folder(param.path_qc)
# check if data, segmentation and landmarks are in the same space
# JULIEN 2017-04-25: removed because of issue #1168
# sct.printv('\nCheck if data, segmentation and landmarks are in the same space...')
# if not sct.check_if_same_space(fname_data, fname_seg):
# sct.printv('ERROR: Data image and segmentation are not in the same space. Please check space and orientation of your files', verbose, 'error')
# if not sct.check_if_same_space(fname_data, fname_landmarks):
# sct.printv('ERROR: Data image and landmarks are not in the same space. Please check space and orientation of your files', verbose, 'error')
# check input labels
labels = check_labels(fname_landmarks)
# create temporary folder
path_tmp = sct.tmp_create(verbose=verbose)
# set temporary file names
ftmp_data = 'data.nii'
ftmp_seg = 'seg.nii.gz'
ftmp_label = 'label.nii.gz'
ftmp_template = 'template.nii'
ftmp_template_seg = 'template_seg.nii.gz'
ftmp_template_label = 'template_label.nii.gz'
# copy files to temporary folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
sct.run('sct_convert -i ' + fname_data + ' -o ' + path_tmp + ftmp_data)
sct.run('sct_convert -i ' + fname_seg + ' -o ' + path_tmp + ftmp_seg)
sct.run('sct_convert -i ' + fname_landmarks + ' -o ' + path_tmp + ftmp_label)
sct.run('sct_convert -i ' + fname_template + ' -o ' + path_tmp + ftmp_template)
sct.run('sct_convert -i ' + fname_template_seg + ' -o ' + path_tmp + ftmp_template_seg)
# sct.run('sct_convert -i '+fname_template_label+' -o '+path_tmp+ftmp_template_label)
# go to tmp folder
os.chdir(path_tmp)
# copy header of anat to segmentation (issue #1168)
# from sct_image import copy_header
# im_data = Image(ftmp_data)
# im_seg = Image(ftmp_seg)
# copy_header(im_data, im_seg)
# im_seg.save()
# im_label = Image(ftmp_label)
# copy_header(im_data, im_label)
# im_label.save()
# Generate labels from template vertebral labeling
sct.printv('\nGenerate labels from template vertebral labeling', verbose)
sct.run('sct_label_utils -i ' + fname_template_vertebral_labeling + ' -vert-body 0 -o ' + ftmp_template_label)
# check if provided labels are available in the template
sct.printv('\nCheck if provided labels are available in the template', verbose)
image_label_template = Image(ftmp_template_label)
labels_template = image_label_template.getNonZeroCoordinates(sorting='value')
if labels[-1].value > labels_template[-1].value:
sct.printv('ERROR: Wrong landmarks input. Labels must have correspondence in template space. \nLabel max '
'provided: ' + str(labels[-1].value) + '\nLabel max from template: ' +
str(labels_template[-1].value), verbose, 'error')
# binarize segmentation (in case it has values below 0 caused by manual editing)
sct.printv('\nBinarize segmentation', verbose)
sct.run('sct_maths -i seg.nii.gz -bin 0.5 -o seg.nii.gz')
# smooth segmentation (jcohenadad, issue #613)
# sct.printv('\nSmooth segmentation...', verbose)
# sct.run('sct_maths -i '+ftmp_seg+' -smooth 1.5 -o '+add_suffix(ftmp_seg, '_smooth'))
# jcohenadad: updated 2016-06-16: DO NOT smooth the seg anymore. Issue #
# sct.run('sct_maths -i '+ftmp_seg+' -smooth 0 -o '+add_suffix(ftmp_seg, '_smooth'))
# ftmp_seg = add_suffix(ftmp_seg, '_smooth')
# Switch between modes: subject->template or template->subject
if ref == 'template':
# resample data to 1mm isotropic
sct.printv('\nResample data to 1mm isotropic...', verbose)
sct.run('sct_resample -i ' + ftmp_data + ' -mm 1.0x1.0x1.0 -x linear -o ' + add_suffix(ftmp_data, '_1mm'))
ftmp_data = add_suffix(ftmp_data, '_1mm')
sct.run('sct_resample -i ' + ftmp_seg + ' -mm 1.0x1.0x1.0 -x linear -o ' + add_suffix(ftmp_seg, '_1mm'))
ftmp_seg = add_suffix(ftmp_seg, '_1mm')
# N.B. resampling of labels is more complicated, because they are single-point labels, therefore resampling with neighrest neighbour can make them disappear. Therefore a more clever approach is required.
resample_labels(ftmp_label, ftmp_data, add_suffix(ftmp_label, '_1mm'))
ftmp_label = add_suffix(ftmp_label, '_1mm')
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
sct.run('sct_image -i ' + ftmp_data + ' -setorient RPI -o ' + add_suffix(ftmp_data, '_rpi'))
ftmp_data = add_suffix(ftmp_data, '_rpi')
sct.run('sct_image -i ' + ftmp_seg + ' -setorient RPI -o ' + add_suffix(ftmp_seg, '_rpi'))
ftmp_seg = add_suffix(ftmp_seg, '_rpi')
sct.run('sct_image -i ' + ftmp_label + ' -setorient RPI -o ' + add_suffix(ftmp_label, '_rpi'))
ftmp_label = add_suffix(ftmp_label, '_rpi')
# get landmarks in native space
# crop segmentation
# output: segmentation_rpi_crop.nii.gz
status_crop, output_crop = sct.run('sct_crop_image -i ' + ftmp_seg + ' -o ' + add_suffix(ftmp_seg, '_crop') + ' -dim 2 -bzmax', verbose)
ftmp_seg = add_suffix(ftmp_seg, '_crop')
cropping_slices = output_crop.split('Dimension 2: ')[1].split('\n')[0].split(' ')
# straighten segmentation
sct.printv('\nStraighten the spinal cord using centerline/segmentation...', verbose)
# 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 ' + ftmp_seg + ' -w warp_curve2straight.nii.gz -d straight_ref.nii.gz -o ' + add_suffix(ftmp_seg, '_straight'))
else:
sct.run('sct_straighten_spinalcord -i ' + ftmp_seg + ' -s ' + ftmp_seg + ' -o ' + add_suffix(ftmp_seg, '_straight') + ' -qc 0 -r 0 -v ' + str(verbose), verbose)
# N.B. DO NOT UPDATE VARIABLE ftmp_seg BECAUSE TEMPORARY USED LATER
# re-define warping field using non-cropped space (to avoid issue #367)
sct.run('sct_concat_transfo -w warp_straight2curve.nii.gz -d ' + ftmp_data + ' -o warp_straight2curve.nii.gz')
# Label preparation:
# --------------------------------------------------------------------------------
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run('sct_label_utils -i ' + ftmp_template_label + ' -o ' + ftmp_template_label + ' -remove ' + ftmp_label)
# Dilating the input label so they can be straighten without losing them
sct.printv('\nDilating input labels using 3vox ball radius')
sct.run('sct_maths -i ' + ftmp_label + ' -o ' + add_suffix(ftmp_label, '_dilate') + ' -dilate 3')
ftmp_label = add_suffix(ftmp_label, '_dilate')
# Apply straightening to labels
sct.printv('\nApply straightening to labels...', verbose)
sct.run('sct_apply_transfo -i ' + ftmp_label + ' -o ' + add_suffix(ftmp_label, '_straight') + ' -d ' + add_suffix(ftmp_seg, '_straight') + ' -w warp_curve2straight.nii.gz -x nn')
ftmp_label = add_suffix(ftmp_label, '_straight')
# Compute rigid transformation straight landmarks --> template landmarks
sct.printv('\nEstimate transformation for step #0...', verbose)
from msct_register_landmarks import register_landmarks
try:
register_landmarks(ftmp_label, ftmp_template_label, paramreg.steps['0'].dof, fname_affine='straight2templateAffine.txt', verbose=verbose)
except Exception:
sct.printv('ERROR: input labels do not seem to be at the right place. Please check the position of the labels. See documentation for more details: https://sourceforge.net/p/spinalcordtoolbox/wiki/create_labels/', verbose=verbose, type='error')
# Concatenate transformations: curve --> straight --> affine
sct.printv('\nConcatenate transformations: curve --> straight --> affine...', verbose)
sct.run('sct_concat_transfo -w warp_curve2straight.nii.gz,straight2templateAffine.txt -d template.nii -o warp_curve2straightAffine.nii.gz')
# Apply transformation
sct.printv('\nApply transformation...', verbose)
sct.run('sct_apply_transfo -i ' + ftmp_data + ' -o ' + add_suffix(ftmp_data, '_straightAffine') + ' -d ' + ftmp_template + ' -w warp_curve2straightAffine.nii.gz')
ftmp_data = add_suffix(ftmp_data, '_straightAffine')
sct.run('sct_apply_transfo -i ' + ftmp_seg + ' -o ' + add_suffix(ftmp_seg, '_straightAffine') + ' -d ' + ftmp_template + ' -w warp_curve2straightAffine.nii.gz -x linear')
ftmp_seg = add_suffix(ftmp_seg, '_straightAffine')
"""
# Benjamin: Issue from Allan Martin, about the z=0 slice that is screwed up, caused by the affine transform.
# Solution found: remove slices below and above landmarks to avoid rotation effects
points_straight = []
for coord in landmark_template:
points_straight.append(coord.z)
min_point, max_point = int(round(np.min(points_straight))), int(round(np.max(points_straight)))
sct.run('sct_crop_image -i ' + ftmp_seg + ' -start ' + str(min_point) + ' -end ' + str(max_point) + ' -dim 2 -b 0 -o ' + add_suffix(ftmp_seg, '_black'))
ftmp_seg = add_suffix(ftmp_seg, '_black')
"""
# binarize
sct.printv('\nBinarize segmentation...', verbose)
sct.run('sct_maths -i ' + ftmp_seg + ' -bin 0.5 -o ' + add_suffix(ftmp_seg, '_bin'))
ftmp_seg = add_suffix(ftmp_seg, '_bin')
# find min-max of anat2template (for subsequent cropping)
zmin_template, zmax_template = find_zmin_zmax(ftmp_seg)
# crop template in z-direction (for faster processing)
sct.printv('\nCrop data in template space (for faster processing)...', verbose)
sct.run('sct_crop_image -i ' + ftmp_template + ' -o ' + add_suffix(ftmp_template, '_crop') + ' -dim 2 -start ' + str(zmin_template) + ' -end ' + str(zmax_template))
ftmp_template = add_suffix(ftmp_template, '_crop')
sct.run('sct_crop_image -i ' + ftmp_template_seg + ' -o ' + add_suffix(ftmp_template_seg, '_crop') + ' -dim 2 -start ' + str(zmin_template) + ' -end ' + str(zmax_template))
ftmp_template_seg = add_suffix(ftmp_template_seg, '_crop')
sct.run('sct_crop_image -i ' + ftmp_data + ' -o ' + add_suffix(ftmp_data, '_crop') + ' -dim 2 -start ' + str(zmin_template) + ' -end ' + str(zmax_template))
ftmp_data = add_suffix(ftmp_data, '_crop')
sct.run('sct_crop_image -i ' + ftmp_seg + ' -o ' + add_suffix(ftmp_seg, '_crop') + ' -dim 2 -start ' + str(zmin_template) + ' -end ' + str(zmax_template))
ftmp_seg = add_suffix(ftmp_seg, '_crop')
# sub-sample in z-direction
sct.printv('\nSub-sample in z-direction (for faster processing)...', verbose)
sct.run('sct_resample -i ' + ftmp_template + ' -o ' + add_suffix(ftmp_template, '_sub') + ' -f 1x1x' + zsubsample, verbose)
ftmp_template = add_suffix(ftmp_template, '_sub')
sct.run('sct_resample -i ' + ftmp_template_seg + ' -o ' + add_suffix(ftmp_template_seg, '_sub') + ' -f 1x1x' + zsubsample, verbose)
ftmp_template_seg = add_suffix(ftmp_template_seg, '_sub')
sct.run('sct_resample -i ' + ftmp_data + ' -o ' + add_suffix(ftmp_data, '_sub') + ' -f 1x1x' + zsubsample, verbose)
ftmp_data = add_suffix(ftmp_data, '_sub')
sct.run('sct_resample -i ' + ftmp_seg + ' -o ' + add_suffix(ftmp_seg, '_sub') + ' -f 1x1x' + zsubsample, verbose)
ftmp_seg = add_suffix(ftmp_seg, '_sub')
# Registration straight spinal cord to template
sct.printv('\nRegister straight spinal cord to template...', verbose)
# loop across registration steps
warp_forward = []
warp_inverse = []
for i_step in range(1, len(paramreg.steps)):
sct.printv('\nEstimate transformation for step #' + str(i_step) + '...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_data
dest = ftmp_template
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_seg
dest = ftmp_template_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# if step>1, apply warp_forward_concat to the src image to be used
if i_step > 1:
# sct.run('sct_apply_transfo -i '+src+' -d '+dest+' -w '+','.join(warp_forward)+' -o '+sct.add_suffix(src, '_reg')+' -x '+interp_step, verbose)
# apply transformation from previous step, to use as new src for registration
sct.run('sct_apply_transfo -i ' + src + ' -d ' + dest + ' -w ' + ','.join(warp_forward) + ' -o ' + add_suffix(src, '_regStep' + str(i_step - 1)) + ' -x ' + interp_step, verbose)
src = add_suffix(src, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.append(warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: anat --> template...', verbose)
sct.run('sct_concat_transfo -w warp_curve2straightAffine.nii.gz,' + ','.join(warp_forward) + ' -d template.nii -o warp_anat2template.nii.gz', verbose)
# sct.run('sct_concat_transfo -w warp_curve2straight.nii.gz,straight2templateAffine.txt,'+','.join(warp_forward)+' -d template.nii -o warp_anat2template.nii.gz', verbose)
sct.printv('\nConcatenate transformations: template --> anat...', verbose)
warp_inverse.reverse()
sct.run('sct_concat_transfo -w ' + ','.join(warp_inverse) + ',-straight2templateAffine.txt,warp_straight2curve.nii.gz -d data.nii -o warp_template2anat.nii.gz', verbose)
# register template->subject
elif ref == 'subject':
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
sct.run('sct_image -i ' + ftmp_data + ' -setorient RPI -o ' + add_suffix(ftmp_data, '_rpi'))
ftmp_data = add_suffix(ftmp_data, '_rpi')
sct.run('sct_image -i ' + ftmp_seg + ' -setorient RPI -o ' + add_suffix(ftmp_seg, '_rpi'))
ftmp_seg = add_suffix(ftmp_seg, '_rpi')
sct.run('sct_image -i ' + ftmp_label + ' -setorient RPI -o ' + add_suffix(ftmp_label, '_rpi'))
ftmp_label = add_suffix(ftmp_label, '_rpi')
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run('sct_label_utils -i ' + ftmp_template_label + ' -o ' + ftmp_template_label + ' -remove ' + ftmp_label)
# Add one label because at least 3 orthogonal labels are required to estimate an affine transformation. This new label is added at the level of the upper most label (lowest value), at 1cm to the right.
for i_file in [ftmp_label, ftmp_template_label]:
im_label = Image(i_file)
coord_label = im_label.getCoordinatesAveragedByValue() # N.B. landmarks are sorted by value
# Create new label
from copy import deepcopy
new_label = deepcopy(coord_label[0])
# move it 5mm to the left (orientation is RAS)
nx, ny, nz, nt, px, py, pz, pt = im_label.dim
new_label.x = round(coord_label[0].x + 5.0 / px)
# assign value 99
new_label.value = 99
# Add to existing image
im_label.data[int(new_label.x), int(new_label.y), int(new_label.z)] = new_label.value
# Overwrite label file
# im_label.setFileName('label_rpi_modif.nii.gz')
im_label.save()
# Bring template to subject space using landmark-based transformation
sct.printv('\nEstimate transformation for step #0...', verbose)
from msct_register_landmarks import register_landmarks
warp_forward = ['template2subjectAffine.txt']
warp_inverse = ['-template2subjectAffine.txt']
try:
register_landmarks(ftmp_template_label, ftmp_label, paramreg.steps['0'].dof, fname_affine=warp_forward[0], verbose=verbose, path_qc=param.path_qc)
except Exception:
sct.printv('ERROR: input labels do not seem to be at the right place. Please check the position of the labels. See documentation for more details: https://sourceforge.net/p/spinalcordtoolbox/wiki/create_labels/', verbose=verbose, type='error')
# loop across registration steps
for i_step in range(1, len(paramreg.steps)):
sct.printv('\nEstimate transformation for step #' + str(i_step) + '...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_template
dest = ftmp_data
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_template_seg
dest = ftmp_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# apply transformation from previous step, to use as new src for registration
sct.run('sct_apply_transfo -i ' + src + ' -d ' + dest + ' -w ' + ','.join(warp_forward) + ' -o ' + add_suffix(src, '_regStep' + str(i_step - 1)) + ' -x ' + interp_step, verbose)
src = add_suffix(src, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.insert(0, warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: template --> subject...', verbose)
sct.run('sct_concat_transfo -w ' + ','.join(warp_forward) + ' -d data.nii -o warp_template2anat.nii.gz', verbose)
sct.printv('\nConcatenate transformations: subject --> template...', verbose)
sct.run('sct_concat_transfo -w ' + ','.join(warp_inverse) + ' -d template.nii -o warp_anat2template.nii.gz', verbose)
# Apply warping fields to anat and template
sct.run('sct_apply_transfo -i template.nii -o template2anat.nii.gz -d data.nii -w warp_template2anat.nii.gz -crop 1', verbose)
sct.run('sct_apply_transfo -i data.nii -o anat2template.nii.gz -d template.nii -w warp_anat2template.nii.gz -crop 1', verbose)
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(path_tmp + 'warp_template2anat.nii.gz', path_output + 'warp_template2anat.nii.gz', verbose)
sct.generate_output_file(path_tmp + 'warp_anat2template.nii.gz', path_output + 'warp_anat2template.nii.gz', verbose)
sct.generate_output_file(path_tmp + 'template2anat.nii.gz', path_output + 'template2anat' + ext_data, verbose)
sct.generate_output_file(path_tmp + 'anat2template.nii.gz', path_output + 'anat2template' + ext_data, verbose)
if ref == 'template':
# copy straightening files in case subsequent SCT functions need them
sct.generate_output_file(path_tmp + 'warp_curve2straight.nii.gz', path_output + 'warp_curve2straight.nii.gz', verbose)
sct.generate_output_file(path_tmp + 'warp_straight2curve.nii.gz', path_output + 'warp_straight2curve.nii.gz', verbose)
sct.generate_output_file(path_tmp + 'straight_ref.nii.gz', path_output + 'straight_ref.nii.gz', verbose)
# Delete temporary files
if remove_temp_files:
sct.printv('\nDelete temporary files...', verbose)
sct.run('rm -rf ' + path_tmp)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(round(elapsed_time))) + 's', verbose)
if '-qc' in arguments and not arguments.get('-noqc', False):
qc_path = arguments['-qc']
import spinalcordtoolbox.reports.qc as qc
import spinalcordtoolbox.reports.slice as qcslice
qc_param = qc.Params(fname_data, 'sct_register_to_template', args, 'Sagittal', qc_path)
report = qc.QcReport(qc_param, '')
@qc.QcImage(report, 'none', [qc.QcImage.no_seg_seg])
def test(qslice):
return qslice.single()
fname_template2anat = path_output + 'template2anat' + ext_data
test(qcslice.SagittalTemplate2Anat(Image(fname_data), Image(fname_template2anat), Image(fname_seg)))
sct.printv('Sucessfully generate the QC results in %s' % qc_param.qc_results)
sct.printv('Use the following command to see the results in a browser')
sct.printv('sct_qc -folder %s' % qc_path, type='info')
# to view results
sct.printv('\nTo view results, type:', verbose)
sct.printv('fslview ' + fname_data + ' ' + path_output + 'template2anat -b 0,4000 &', verbose, 'info')
sct.printv('fslview ' + fname_template + ' -b 0,5000 ' + path_output + 'anat2template &\n', verbose, 'info')
def main():
parser = get_parser()
param = Param()
""" Rewrite arguments and set parameters"""
arguments = parser.parse(sys.argv[1:])
(fname_data, fname_landmarks, path_output, path_template, contrast_template, ref, remove_temp_files,
verbose, init_labels, first_label,nb_slice_to_mean)=rewrite_arguments(arguments)
(param, paramreg)=write_paramaters(arguments,param,ref,verbose)
if(init_labels):
use_viewer_to_define_labels(fname_data,first_label,nb_slice_to_mean)
# initialize other parameters
# file_template_label = param.file_template_label
zsubsample = param.zsubsample
template = os.path.basename(os.path.normpath(pth_template))
# smoothing_sigma = param.smoothing_sigma
# retrieve template file names
from sct_warp_template import get_file_label
file_template_vertebral_labeling = get_file_label(path_template+'template/', 'vertebral')
file_template = get_file_label(path_template+'template/', contrast_template.upper()+'-weighted')
file_template_seg = get_file_label(path_template+'template/', 'spinal cord')
""" Start timer"""
start_time = time.time()
""" Manage file of templates"""
(fname_template, fname_template_vertebral_labeling, fname_template_seg)=make_fname_of_templates(file_template,path_template,file_template_vertebral_labeling,file_template_seg)
check_do_files_exist(fname_template,fname_template_vertebral_labeling,fname_template_seg,verbose)
sct.printv(arguments(verbose, fname_data, fname_landmarks, fname_seg, path_template, remove_temp_files))
""" Create QC folder """
sct.create_folder(param.path_qc)
""" Check if data, segmentation and landmarks are in the same space"""
(ext_data, path_data, file_data)=check_data_segmentation_landmarks_same_space(fname_data, fname_seg, fname_landmarks,verbose)
''' Check input labels'''
labels = check_labels(fname_landmarks)
""" Create temporary folder, set temporary file names, copy files into it and go in it """
path_tmp = sct.tmp_create(verbose=verbose)
(ftmp_data, ftmp_seg, ftmp_label, ftmp_template, ftmp_template_seg, ftmp_template_label)=set_temporary_files()
copy_files_to_temporary_files(verbose, fname_data, path_tmp, ftmp_seg, ftmp_data, fname_seg, fname_landmarks,
ftmp_label, fname_template, ftmp_template, fname_template_seg, ftmp_template_seg)
os.chdir(path_tmp)
''' Generate labels from template vertebral labeling'''
sct.printv('\nGenerate labels from template vertebral labeling', verbose)
sct.run('sct_label_utils -i '+fname_template_vertebral_labeling+' -vert-body 0 -o '+ftmp_template_label)
''' Check if provided labels are available in the template'''
sct.printv('\nCheck if provided labels are available in the template', verbose)
image_label_template = Image(ftmp_template_label)
labels_template = image_label_template.getNonZeroCoordinates(sorting='value')
if labels[-1].value > labels_template[-1].value:
sct.printv('ERROR: Wrong landmarks input. Labels must have correspondence in template space. \nLabel max '
'provided: ' + str(labels[-1].value) + '\nLabel max from template: ' +
str(labels_template[-1].value), verbose, 'error')
''' Binarize segmentation (in case it has values below 0 caused by manual editing)'''
sct.printv('\nBinarize segmentation', verbose)
sct.run('sct_maths -i seg.nii.gz -bin 0.5 -o seg.nii.gz')
# smooth segmentation (jcohenadad, issue #613)
# sct.printv('\nSmooth segmentation...', verbose)
# sct.run('sct_maths -i '+ftmp_seg+' -smooth 1.5 -o '+add_suffix(ftmp_seg, '_smooth'))
# jcohenadad: updated 2016-06-16: DO NOT smooth the seg anymore. Issue #
# sct.run('sct_maths -i '+ftmp_seg+' -smooth 0 -o '+add_suffix(ftmp_seg, '_smooth'))
# ftmp_seg = add_suffix(ftmp_seg, '_smooth')
# Switch between modes: subject->template or template->subject
if ref == 'template':
# resample data to 1mm isotropic
sct.printv('\nResample data to 1mm isotropic...', verbose)
sct.run('sct_resample -i '+ftmp_data+' -mm 1.0x1.0x1.0 -x linear -o '+add_suffix(ftmp_data, '_1mm'))
ftmp_data = add_suffix(ftmp_data, '_1mm')
sct.run('sct_resample -i '+ftmp_seg+' -mm 1.0x1.0x1.0 -x linear -o '+add_suffix(ftmp_seg, '_1mm'))
ftmp_seg = add_suffix(ftmp_seg, '_1mm')
# N.B. resampling of labels is more complicated, because they are single-point labels, therefore resampling with neighrest neighbour can make them disappear. Therefore a more clever approach is required.
resample_labels(ftmp_label, ftmp_data, add_suffix(ftmp_label, '_1mm'))
ftmp_label = add_suffix(ftmp_label, '_1mm')
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
sct.run('sct_image -i '+ftmp_data+' -setorient RPI -o '+add_suffix(ftmp_data, '_rpi'))
ftmp_data = add_suffix(ftmp_data, '_rpi')
sct.run('sct_image -i '+ftmp_seg+' -setorient RPI -o '+add_suffix(ftmp_seg, '_rpi'))
ftmp_seg = add_suffix(ftmp_seg, '_rpi')
sct.run('sct_image -i '+ftmp_label+' -setorient RPI -o '+add_suffix(ftmp_label, '_rpi'))
ftmp_label = add_suffix(ftmp_label, '_rpi')
# get landmarks in native space
# crop segmentation
# output: segmentation_rpi_crop.nii.gz
status_crop, output_crop = sct.run('sct_crop_image -i '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_crop')+' -dim 2 -bzmax', verbose)
ftmp_seg = add_suffix(ftmp_seg, '_crop')
cropping_slices = output_crop.split('Dimension 2: ')[1].split('\n')[0].split(' ')
# straighten segmentation
sct.printv('\nStraighten the spinal cord using centerline/segmentation...', verbose)
# 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 '+ftmp_seg+' -w warp_curve2straight.nii.gz -d straight_ref.nii.gz -o '+add_suffix(ftmp_seg, '_straight'))
else:
sct.run('sct_straighten_spinalcord -i '+ftmp_seg+' -s '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_straight')+' -qc 0 -r 0 -v '+str(verbose), verbose)
# N.B. DO NOT UPDATE VARIABLE ftmp_seg BECAUSE TEMPORARY USED LATER
# re-define warping field using non-cropped space (to avoid issue #367)
sct.run('sct_concat_transfo -w warp_straight2curve.nii.gz -d '+ftmp_data+' -o warp_straight2curve.nii.gz')
# Label preparation:
# --------------------------------------------------------------------------------
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run('sct_label_utils -i '+ftmp_template_label+' -o '+ftmp_template_label+' -remove '+ftmp_label)
# Dilating the input label so they can be straighten without losing them
sct.printv('\nDilating input labels using 3vox ball radius')
sct.run('sct_maths -i '+ftmp_label+' -o '+add_suffix(ftmp_label, '_dilate')+' -dilate 3')
ftmp_label = add_suffix(ftmp_label, '_dilate')
# Apply straightening to labels
sct.printv('\nApply straightening to labels...', verbose)
sct.run('sct_apply_transfo -i '+ftmp_label+' -o '+add_suffix(ftmp_label, '_straight')+' -d '+add_suffix(ftmp_seg, '_straight')+' -w warp_curve2straight.nii.gz -x nn')
ftmp_label = add_suffix(ftmp_label, '_straight')
# Compute rigid transformation straight landmarks --> template landmarks
sct.printv('\nEstimate transformation for step #0...', verbose)
from msct_register_landmarks import register_landmarks
try:
register_landmarks(ftmp_label, ftmp_template_label, paramreg.steps['0'].dof, fname_affine='straight2templateAffine.txt', verbose=verbose)
except Exception:
sct.printv('ERROR: input labels do not seem to be at the right place. Please check the position of the labels. See documentation for more details: https://sourceforge.net/p/spinalcordtoolbox/wiki/create_labels/', verbose=verbose, type='error')
# Concatenate transformations: curve --> straight --> affine
sct.printv('\nConcatenate transformations: curve --> straight --> affine...', verbose)
sct.run('sct_concat_transfo -w warp_curve2straight.nii.gz,straight2templateAffine.txt -d template.nii -o warp_curve2straightAffine.nii.gz')
# Apply transformation
sct.printv('\nApply transformation...', verbose)
sct.run('sct_apply_transfo -i '+ftmp_data+' -o '+add_suffix(ftmp_data, '_straightAffine')+' -d '+ftmp_template+' -w warp_curve2straightAffine.nii.gz')
ftmp_data = add_suffix(ftmp_data, '_straightAffine')
sct.run('sct_apply_transfo -i '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_straightAffine')+' -d '+ftmp_template+' -w warp_curve2straightAffine.nii.gz -x linear')
ftmp_seg = add_suffix(ftmp_seg, '_straightAffine')
"""
# Benjamin: Issue from Allan Martin, about the z=0 slice that is screwed up, caused by the affine transform.
# Solution found: remove slices below and above landmarks to avoid rotation effects
points_straight = []
for coord in landmark_template:
points_straight.append(coord.z)
min_point, max_point = int(round(np.min(points_straight))), int(round(np.max(points_straight)))
sct.run('sct_crop_image -i ' + ftmp_seg + ' -start ' + str(min_point) + ' -end ' + str(max_point) + ' -dim 2 -b 0 -o ' + add_suffix(ftmp_seg, '_black'))
ftmp_seg = add_suffix(ftmp_seg, '_black')
"""
# binarize
sct.printv('\nBinarize segmentation...', verbose)
sct.run('sct_maths -i '+ftmp_seg+' -bin 0.5 -o '+add_suffix(ftmp_seg, '_bin'))
ftmp_seg = add_suffix(ftmp_seg, '_bin')
# find min-max of anat2template (for subsequent cropping)
zmin_template, zmax_template = find_zmin_zmax(ftmp_seg)
# crop template in z-direction (for faster processing)
sct.printv('\nCrop data in template space (for faster processing)...', verbose)
sct.run('sct_crop_image -i '+ftmp_template+' -o '+add_suffix(ftmp_template, '_crop')+' -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
ftmp_template = add_suffix(ftmp_template, '_crop')
sct.run('sct_crop_image -i '+ftmp_template_seg+' -o '+add_suffix(ftmp_template_seg, '_crop')+' -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
ftmp_template_seg = add_suffix(ftmp_template_seg, '_crop')
sct.run('sct_crop_image -i '+ftmp_data+' -o '+add_suffix(ftmp_data, '_crop')+' -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
ftmp_data = add_suffix(ftmp_data, '_crop')
sct.run('sct_crop_image -i '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_crop')+' -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
ftmp_seg = add_suffix(ftmp_seg, '_crop')
# sub-sample in z-direction
sct.printv('\nSub-sample in z-direction (for faster processing)...', verbose)
sct.run('sct_resample -i '+ftmp_template+' -o '+add_suffix(ftmp_template, '_sub')+' -f 1x1x'+zsubsample, verbose)
ftmp_template = add_suffix(ftmp_template, '_sub')
sct.run('sct_resample -i '+ftmp_template_seg+' -o '+add_suffix(ftmp_template_seg, '_sub')+' -f 1x1x'+zsubsample, verbose)
ftmp_template_seg = add_suffix(ftmp_template_seg, '_sub')
sct.run('sct_resample -i '+ftmp_data+' -o '+add_suffix(ftmp_data, '_sub')+' -f 1x1x'+zsubsample, verbose)
ftmp_data = add_suffix(ftmp_data, '_sub')
sct.run('sct_resample -i '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_sub')+' -f 1x1x'+zsubsample, verbose)
ftmp_seg = add_suffix(ftmp_seg, '_sub')
# Registration straight spinal cord to template
sct.printv('\nRegister straight spinal cord to template...', verbose)
# loop across registration steps
warp_forward = []
warp_inverse = []
for i_step in range(1, len(paramreg.steps)):
sct.printv('\nEstimate transformation for step #'+str(i_step)+'...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_data
dest = ftmp_template
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_seg
dest = ftmp_template_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# if step>1, apply warp_forward_concat to the src image to be used
if i_step > 1:
# sct.run('sct_apply_transfo -i '+src+' -d '+dest+' -w '+','.join(warp_forward)+' -o '+sct.add_suffix(src, '_reg')+' -x '+interp_step, verbose)
# apply transformation from previous step, to use as new src for registration
sct.run('sct_apply_transfo -i '+src+' -d '+dest+' -w '+','.join(warp_forward)+' -o '+add_suffix(src, '_regStep'+str(i_step-1))+' -x '+interp_step, verbose)
src = add_suffix(src, '_regStep'+str(i_step-1))
# register src --> dest
# TODO: display param for debugging
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.append(warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: anat --> template...', verbose)
sct.run('sct_concat_transfo -w warp_curve2straightAffine.nii.gz,'+','.join(warp_forward)+' -d template.nii -o warp_anat2template.nii.gz', verbose)
# sct.run('sct_concat_transfo -w warp_curve2straight.nii.gz,straight2templateAffine.txt,'+','.join(warp_forward)+' -d template.nii -o warp_anat2template.nii.gz', verbose)
sct.printv('\nConcatenate transformations: template --> anat...', verbose)
warp_inverse.reverse()
sct.run('sct_concat_transfo -w '+','.join(warp_inverse)+',-straight2templateAffine.txt,warp_straight2curve.nii.gz -d data.nii -o warp_template2anat.nii.gz', verbose)
# register template->subject
elif ref == 'subject':
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
sct.run('sct_image -i ' + ftmp_data + ' -setorient RPI -o ' + add_suffix(ftmp_data, '_rpi'))
ftmp_data = add_suffix(ftmp_data, '_rpi')
sct.run('sct_image -i ' + ftmp_seg + ' -setorient RPI -o ' + add_suffix(ftmp_seg, '_rpi'))
ftmp_seg = add_suffix(ftmp_seg, '_rpi')
sct.run('sct_image -i ' + ftmp_label + ' -setorient RPI -o ' + add_suffix(ftmp_label, '_rpi'))
ftmp_label = add_suffix(ftmp_label, '_rpi')
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run('sct_label_utils -i '+ftmp_template_label+' -o '+ftmp_template_label+' -remove '+ftmp_label)
# Add one label because at least 3 orthogonal labels are required to estimate an affine transformation. This new label is added at the level of the upper most label (lowest value), at 1cm to the right.
for i_file in [ftmp_label, ftmp_template_label]:
im_label = Image(i_file)
coord_label = im_label.getCoordinatesAveragedByValue() # N.B. landmarks are sorted by value
# Create new label
from copy import deepcopy
new_label = deepcopy(coord_label[0])
# move it 5mm to the left (orientation is RAS)
nx, ny, nz, nt, px, py, pz, pt = im_label.dim
new_label.x = round(coord_label[0].x + 5.0 / px)
# assign value 99
new_label.value = 99
# Add to existing image
im_label.data[int(new_label.x), int(new_label.y), int(new_label.z)] = new_label.value
# Overwrite label file
# im_label.setFileName('label_rpi_modif.nii.gz')
im_label.save()
# Bring template to subject space using landmark-based transformation
sct.printv('\nEstimate transformation for step #0...', verbose)
from msct_register_landmarks import register_landmarks
warp_forward = ['template2subjectAffine.txt']
warp_inverse = ['-template2subjectAffine.txt']
try:
register_landmarks(ftmp_template_label, ftmp_label, paramreg.steps['0'].dof, fname_affine=warp_forward[0], verbose=verbose, path_qc=param.path_qc)
except Exception:
sct.printv('ERROR: input labels do not seem to be at the right place. Please check the position of the labels. See documentation for more details: https://sourceforge.net/p/spinalcordtoolbox/wiki/create_labels/', verbose=verbose, type='error')
# loop across registration steps
for i_step in range(1, len(paramreg.steps)):
sct.printv('\nEstimate transformation for step #'+str(i_step)+'...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_template
dest = ftmp_data
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_template_seg
dest = ftmp_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# apply transformation from previous step, to use as new src for registration
sct.run('sct_apply_transfo -i '+src+' -d '+dest+' -w '+','.join(warp_forward)+' -o '+add_suffix(src, '_regStep'+str(i_step-1))+' -x '+interp_step, verbose)
src = add_suffix(src, '_regStep'+str(i_step-1))
# register src --> dest
# TODO: display param for debugging
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.insert(0, warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: template --> subject...', verbose)
sct.run('sct_concat_transfo -w '+','.join(warp_forward)+' -d data.nii -o warp_template2anat.nii.gz', verbose)
sct.printv('\nConcatenate transformations: subject --> template...', verbose)
sct.run('sct_concat_transfo -w '+','.join(warp_inverse)+' -d template.nii -o warp_anat2template.nii.gz', verbose)
# Apply warping fields to anat and template
sct.run('sct_apply_transfo -i template.nii -o template2anat.nii.gz -d data.nii -w warp_template2anat.nii.gz -crop 1', verbose)
sct.run('sct_apply_transfo -i data.nii -o anat2template.nii.gz -d template.nii -w warp_anat2template.nii.gz -crop 1', verbose)
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(path_tmp+'warp_template2anat.nii.gz', path_output+'warp_template2anat.nii.gz', verbose)
sct.generate_output_file(path_tmp+'warp_anat2template.nii.gz', path_output+'warp_anat2template.nii.gz', verbose)
sct.generate_output_file(path_tmp+'template2anat.nii.gz', path_output+'template2anat'+ext_data, verbose)
sct.generate_output_file(path_tmp+'anat2template.nii.gz', path_output+'anat2template'+ext_data, verbose)
if ref == 'template':
# copy straightening files in case subsequent SCT functions need them
sct.generate_output_file(path_tmp+'warp_curve2straight.nii.gz', path_output+'warp_curve2straight.nii.gz', verbose)
sct.generate_output_file(path_tmp+'warp_straight2curve.nii.gz', path_output+'warp_straight2curve.nii.gz', verbose)
sct.generate_output_file(path_tmp+'straight_ref.nii.gz', path_output+'straight_ref.nii.gz', verbose)
# Delete temporary files
if remove_temp_files:
sct.printv('\nDelete temporary files...', verbose)
sct.run('rm -rf '+path_tmp)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: '+str(int(round(elapsed_time)))+'s', verbose)
# to view results
sct.printv('\nTo view results, type:', verbose)
sct.printv('fslview '+fname_data+' '+path_output+'template2anat -b 0,4000 &', verbose, 'info')
sct.printv('fslview '+fname_template+' -b 0,5000 '+path_output+'anat2template &\n', verbose, 'info')
def test(path_data='', parameters=''):
verbose = 0
dice_threshold = 0.9
add_path_for_template = False # if absolute path or no path to template is provided, then path to data should not be added.
# initializations
dice_template2anat = float('NaN')
dice_anat2template = float('NaN')
output = ''
if not parameters:
parameters = '-i t2/t2.nii.gz -l t2/labels.nii.gz -s t2/t2_seg.nii.gz ' \
'-param step=1,type=seg,algo=centermassrot,metric=MeanSquares:step=2,type=seg,algo=bsplinesyn,iter=5,metric=MeanSquares ' \
'-t template/ -r 0'
add_path_for_template = True # in this case, path to data should be added
parser = sct_register_to_template.get_parser()
dict_param = parser.parse(parameters.split(), check_file_exist=False)
if add_path_for_template:
dict_param_with_path = parser.add_path_to_file(deepcopy(dict_param), path_data, input_file=True)
else:
dict_param_with_path = parser.add_path_to_file(deepcopy(dict_param), path_data, input_file=True, do_not_add_path=['-t'])
param_with_path = parser.dictionary_to_string(dict_param_with_path)
# Check if input files exist
if not (os.path.isfile(dict_param_with_path['-i']) and
os.path.isfile(dict_param_with_path['-l']) and
os.path.isfile(dict_param_with_path['-s'])):
status = 200
output = 'ERROR: the file(s) provided to test function do not exist in folder: ' + path_data
return status, output, DataFrame(data={'status': int(status), 'output': output}, index=[path_data])
# return status, output, DataFrame(
# data={'status': status, 'output': output,
# 'dice_template2anat': float('nan'), 'dice_anat2template': float('nan')},
# index=[path_data])
# if template is not specified, use default
# if not os.path.isdir(dict_param_with_path['-t']):
# status, path_sct = commands.getstatusoutput('echo $SCT_DIR')
# dict_param_with_path['-t'] = path_sct + default_template
# param_with_path = parser.dictionary_to_string(dict_param_with_path)
# get contrast folder from -i option.
# We suppose we can extract it as the first object when spliting with '/' delimiter.
contrast_folder = ''
input_filename = ''
if dict_param['-i'][0] == '/':
dict_param['-i'] = dict_param['-i'][1:]
input_split = dict_param['-i'].split('/')
if len(input_split) == 2:
contrast_folder = input_split[0] + '/'
input_filename = input_split[1]
else:
input_filename = input_split[0]
if not contrast_folder: # if no contrast folder, send error.
status = 201
output = 'ERROR: when extracting the contrast folder from input file in command line: ' + dict_param['-i'] + ' for ' + path_data
return status, output, DataFrame(data={'status': int(status), 'output': output}, index=[path_data])
# return status, output, DataFrame(
# data={'status': status, 'output': output, 'dice_template2anat': float('nan'), 'dice_anat2template': float('nan')}, index=[path_data])
import time, random
subject_folder = path_data.split('/')
if subject_folder[-1] == '' and len(subject_folder) > 1:
subject_folder = subject_folder[-2]
else:
subject_folder = subject_folder[-1]
path_output = sct.slash_at_the_end('sct_register_to_template_' + subject_folder + '_' + time.strftime("%y%m%d%H%M%S") + '_'+str(random.randint(1, 1000000)), slash=1)
param_with_path += ' -ofolder ' + path_output
cmd = 'sct_register_to_template ' + param_with_path
output += '\n====================================================================================================\n'+cmd+'\n====================================================================================================\n\n' # copy command
time_start = time.time()
try:
status, o = sct.run(cmd, verbose)
except:
status, o = 1, 'ERROR: Function crashed!'
output += o
duration = time.time() - time_start
# if command ran without error, test integrity
if status == 0:
# get filename_template_seg
fname_template_seg = get_file_label(sct.slash_at_the_end(dict_param_with_path['-t'], 1) + 'template/', 'spinal cord', output='filewithpath')
# apply transformation to binary mask: template --> anat
sct.run(
'sct_apply_transfo -i ' + fname_template_seg +
' -d ' + dict_param_with_path['-s'] +
' -w ' + path_output + 'warp_template2anat.nii.gz' +
' -o ' + path_output + 'test_template2anat.nii.gz -x nn', verbose)
# apply transformation to binary mask: anat --> template
sct.run(
'sct_apply_transfo -i ' + dict_param_with_path['-s'] +
' -d ' + fname_template_seg +
' -w ' + path_output + 'warp_anat2template.nii.gz' +
' -o ' + path_output + 'test_anat2template.nii.gz -x nn', verbose)
# compute dice coefficient between template segmentation warped into anat and segmentation from anat
cmd = 'sct_dice_coefficient -i ' + dict_param_with_path['-s'] + ' -d ' + path_output + 'test_template2anat.nii.gz'
status1, output1 = sct.run(cmd, verbose)
# parse output and compare to acceptable threshold
dice_template2anat = float(output1.split('3D Dice coefficient = ')[1].split('\n')[0])
if dice_template2anat < dice_threshold:
status1 = 99
# compute dice coefficient between segmentation from anat warped into template and template segmentation
# N.B. here we use -bmax because the FOV of the anat is smaller than the template
cmd = 'sct_dice_coefficient -i ' + fname_template_seg + ' -d ' + path_output + 'test_anat2template.nii.gz -bmax 1'
status2, output2 = sct.run(cmd, verbose)
# parse output and compare to acceptable threshold
dice_anat2template = float(output2.split('3D Dice coefficient = ')[1].split('\n')[0])
if dice_anat2template < dice_threshold:
status2 = 99
# check if at least one integrity status was equal to 99
if status1 == 99 or status2 == 99:
status = 99
# concatenate outputs
output = output + output1 + output2
# transform results into Pandas structure
results = DataFrame(data={'status': int(status), 'output': output, 'dice_template2anat': dice_template2anat, 'dice_anat2template': dice_anat2template, 'duration [s]': duration}, index=[path_data])
return status, output, results
def vertebral_detection(fname,
fname_seg,
contrast,
param,
init_disc,
verbose=1,
path_template='',
initc2='auto',
path_output='../'):
"""
Find intervertebral discs in straightened image using template matching
:param fname:
:param fname_seg:
:param contrast:
:param param: advanced parameters
:param init_disc:
:param verbose:
:param path_template:
:param path_output: output path for verbose=2 pictures
:return:
"""
sct.printv('\nLook for template...', verbose)
# if path_template == '':
# # get path of SCT
# from os import path
# path_script = path.dirname(__file__)
# path_sct = slash_at_the_end(path.dirname(path_script), 1)
# folder_template = 'data/template/'
# path_template = path_sct+folder_template
sct.printv('Path template: ' + path_template, verbose)
# adjust file names if MNI-Poly-AMU template is used
fname_level = get_file_label(path_template + 'template/',
'vertebral',
output='filewithpath')
fname_template = get_file_label(path_template + 'template/',
contrast.upper() + '-weighted',
output='filewithpath')
# if not len(glob(path_template+'MNI-Poly-AMU*.*')) == 0:
# contrast = contrast.upper()
# file_level = '*_level.nii.gz'
# else:
# file_level = '*_levels.nii.gz'
#
# # retrieve file_template based on contrast
# try:
# fname_template_list = glob(path_template + '*' + contrast + '.nii.gz')
# fname_template = fname_template_list[0]
# except IndexError:
# sct.printv('\nERROR: No template found. Please check the provided path.', 1, 'error')
# retrieve disc level from template
# try:
# fname_level_list = glob(path_template+file_level)
# fname_level = fname_level_list[0]
# except IndexError:
# sct.printv('\nERROR: File *_levels.nii.gz not found.', 1, 'error')
# Open template and vertebral levels
sct.printv('\nOpen template and vertebral levels...', verbose)
data_template = Image(fname_template).data
data_disc_template = Image(fname_level).data
# open anatomical volume
im_input = Image(fname)
data = im_input.data
# smooth data
from scipy.ndimage.filters import gaussian_filter
data = gaussian_filter(data,
param.smooth_factor,
output=None,
mode="reflect")
# get dimension of src
nx, ny, nz = data.shape
# define xc and yc (centered in the field of view)
xc = int(round(nx / 2)) # direction RL
yc = int(round(ny / 2)) # direction AP
# get dimension of template
nxt, nyt, nzt = data_template.shape
# define xc and yc (centered in the field of view)
xct = int(round(nxt / 2)) # direction RL
yct = int(round(nyt / 2)) # direction AP
# define mean distance (in voxel) between adjacent discs: [C1/C2 -> C2/C3], [C2/C3 -> C4/C5], ..., [L1/L2 -> L2/L3]
centerline_level = data_disc_template[xct, yct, :]
# attribute value to each disc. Starts from max level, then decrease.
min_level = centerline_level[centerline_level.nonzero()].min()
max_level = centerline_level[centerline_level.nonzero()].max()
list_disc_value_template = range(min_level, max_level)
# add disc above top one
list_disc_value_template.insert(int(0), min_level - 1)
sct.printv('\nDisc values from template: ' + str(list_disc_value_template),
verbose)
# get diff to find transitions (i.e., discs)
diff_centerline_level = np.diff(centerline_level)
# get disc z-values
list_disc_z_template = diff_centerline_level.nonzero()[0].tolist()
list_disc_z_template.reverse()
sct.printv('Z-values for each disc: ' + str(list_disc_z_template), verbose)
list_distance_template = (
np.diff(list_disc_z_template) *
(-1)).tolist() # multiplies by -1 to get positive distances
sct.printv(
'Distances between discs (in voxel): ' + str(list_distance_template),
verbose)
# if automatic mode, find C2/C3 disc
if init_disc == [] and initc2 == 'auto':
sct.printv('\nDetect C2/C3 disk...', verbose)
zrange = range(0, nz)
ind_c2 = list_disc_value_template.index(2)
z_peak = compute_corr_3d(data,
data_template,
x=xc,
xshift=0,
xsize=param.size_RL_initc2,
y=yc,
yshift=param.shift_AP_initc2,
ysize=param.size_AP_initc2,
z=0,
zshift=param.shift_IS_initc2,
zsize=param.size_IS_initc2,
xtarget=xct,
ytarget=yct,
ztarget=list_disc_z_template[ind_c2],
zrange=zrange,
verbose=verbose,
save_suffix='_initC2',
gaussian_std=param.gaussian_std,
path_output=path_output)
init_disc = [z_peak, 2]
# if manual mode, open viewer for user to click on C2/C3 disc
if init_disc == [] and initc2 == 'manual':
from spinalcordtoolbox.gui.base import AnatomicalParams
from spinalcordtoolbox.gui.sagittal import launch_sagittal_dialog
params = AnatomicalParams()
params.num_points = 1
params.vertebraes = [
3,
]
input_file = Image(fname)
output_file = input_file.copy()
output_file.data *= 0
output_file.setFileName(os.path.join(path_output, 'labels.nii.gz'))
controller = launch_sagittal_dialog(input_file, output_file, params)
mask_points = controller.as_string()
# assign new init_disc_z value, which corresponds to the first vector of mask_points. Note, we need to substract from nz due to SAL orientation: in the viewer, orientation is S-I while in this code, it is I-S.
init_disc = [nz - int(mask_points.split(',')[0]), 2]
# display init disc
if verbose == 2:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
# get percentile for automatic contrast adjustment
data_display = np.mean(data[xc - param.size_RL:xc +
param.size_RL, :, :],
axis=0).transpose()
percmin = np.percentile(data_display, 10)
percmax = np.percentile(data_display, 90)
# display image
plt.matshow(data_display,
fignum=50,
cmap=plt.cm.gray,
clim=[percmin, percmax],
origin='lower')
plt.title('Anatomical image')
plt.autoscale(
enable=False) # to prevent autoscale of axis when displaying plot
plt.figure(50), plt.scatter(yc + param.shift_AP_visu,
init_disc[0],
c='yellow',
s=50)
plt.text(yc + param.shift_AP_visu + 4,
init_disc[0],
str(init_disc[1]) + '/' + str(init_disc[1] + 1),
verticalalignment='center',
horizontalalignment='left',
color='pink',
fontsize=15), plt.draw()
# plt.ion() # enables interactive mode
# FIND DISCS
# ===========================================================================
sct.printv('\nDetect intervertebral discs...', verbose)
# assign initial z and disc
current_z = init_disc[0]
current_disc = init_disc[1]
# mean_distance = mean_distance * pz
# mean_distance_real = np.zeros(len(mean_distance))
# create list for z and disc
list_disc_z = []
list_disc_value = []
zrange = range(-10, 10)
direction = 'superior'
search_next_disc = True
while search_next_disc:
sct.printv(
'Current disc: ' + str(current_disc) + ' (z=' + str(current_z) +
'). Direction: ' + direction, verbose)
try:
# get z corresponding to current disc on template
current_z_template = list_disc_z_template[current_disc]
except:
# in case reached the bottom (see issue #849)
sct.printv(
'WARNING: Reached the bottom of the template. Stop searching.',
verbose, 'warning')
break
# find next disc
# N.B. Do not search for C1/C2 disc (because poorly visible), use template distance instead
if current_disc != 1:
current_z = compute_corr_3d(data,
data_template,
x=xc,
xshift=0,
xsize=param.size_RL,
y=yc,
yshift=param.shift_AP,
ysize=param.size_AP,
z=current_z,
zshift=0,
zsize=param.size_IS,
xtarget=xct,
ytarget=yct,
ztarget=current_z_template,
zrange=zrange,
verbose=verbose,
save_suffix='_disc' +
str(current_disc),
gaussian_std=999,
path_output=path_output)
# display new disc
if verbose == 2:
plt.figure(50), plt.scatter(yc + param.shift_AP_visu,
current_z,
c='yellow',
s=50)
plt.text(yc + param.shift_AP_visu + 4,
current_z,
str(current_disc) + '/' + str(current_disc + 1),
verticalalignment='center',
horizontalalignment='left',
color='yellow',
fontsize=15), plt.draw()
# append to main list
if direction == 'superior':
# append at the beginning
list_disc_z.insert(0, current_z)
list_disc_value.insert(0, current_disc)
elif direction == 'inferior':
# append at the end
list_disc_z.append(current_z)
list_disc_value.append(current_disc)
# adjust correcting factor based on already-identified discs
if len(list_disc_z) > 1:
# compute distance between already-identified discs
list_distance_current = (np.diff(list_disc_z) * (-1)).tolist()
# retrieve the template distance corresponding to the already-identified discs
index_disc_identified = [
i for i, j in enumerate(list_disc_value_template)
if j in list_disc_value[:-1]
]
list_distance_template_identified = [
list_distance_template[i] for i in index_disc_identified
]
# divide subject and template distances for the identified discs
list_subject_to_template_distance = [
float(list_distance_current[i]) /
list_distance_template_identified[i]
for i in range(len(list_distance_current))
]
# average across identified discs to obtain an average correcting factor
correcting_factor = np.mean(list_subject_to_template_distance)
sct.printv('.. correcting factor: ' + str(correcting_factor),
verbose)
else:
correcting_factor = 1
# update list_distance specific for the subject
list_distance = [
int(round(list_distance_template[i] * correcting_factor))
for i in range(len(list_distance_template))
]
# updated average_disc_distance (in case it is needed)
# average_disc_distance = int(round(np.mean(list_distance)))
# assign new current_z and disc value
if direction == 'superior':
try:
approx_distance_to_next_disc = list_distance[
list_disc_value_template.index(current_disc - 1)]
except ValueError:
sct.printv(
'WARNING: Disc value not included in template. Using previously-calculated distance: '
+ str(approx_distance_to_next_disc))
# assign new current_z and disc value
current_z = current_z + approx_distance_to_next_disc
current_disc = current_disc - 1
elif direction == 'inferior':
try:
approx_distance_to_next_disc = list_distance[
list_disc_value_template.index(current_disc)]
except:
sct.printv(
'WARNING: Disc value not included in template. Using previously-calculated distance: '
+ str(approx_distance_to_next_disc))
# assign new current_z and disc value
current_z = current_z - approx_distance_to_next_disc
current_disc = current_disc + 1
# if current_z is larger than searching zone, switch direction (and start from initial z minus approximate distance from updated template distance)
if current_z >= nz or current_disc == 0:
sct.printv('.. Switching to inferior direction.', verbose)
direction = 'inferior'
current_disc = init_disc[1] + 1
current_z = init_disc[0] - list_distance[
list_disc_value_template.index(current_disc)]
# if current_z is lower than searching zone, stop searching
if current_z <= 0:
search_next_disc = False
# if verbose == 2:
# # close figures
# plt.figure(fig_corr), plt.close()
# plt.figure(fig_pattern), plt.close()
# if upper disc is not 1, add disc above top disc based on mean_distance_adjusted
upper_disc = min(list_disc_value)
# if not upper_disc == 1:
sct.printv(
'Adding top disc based on adjusted template distance: #' +
str(upper_disc - 1), verbose)
approx_distance_to_next_disc = list_distance[
list_disc_value_template.index(upper_disc - 1)]
next_z = max(list_disc_z) + approx_distance_to_next_disc
sct.printv('.. approximate distance: ' + str(approx_distance_to_next_disc),
verbose)
# make sure next disc does not go beyond FOV in superior direction
if next_z > nz:
list_disc_z.insert(0, nz)
else:
list_disc_z.insert(0, next_z)
# assign disc value
list_disc_value.insert(0, upper_disc - 1)
# Label segmentation
label_segmentation(fname_seg,
list_disc_z,
list_disc_value,
verbose=verbose)
# save figure
if verbose == 2:
plt.figure(50), plt.savefig(path_output +
'fig_anat_straight_with_labels.png')
def test(path_data='', parameters=''):
verbose = 0
dice_threshold = 0.9
add_path_for_template = False # if absolute path or no path to template is provided, then path to data should not be added.
# initializations
dice_template2anat = float('NaN')
dice_anat2template = float('NaN')
output = ''
if not parameters:
parameters = '-i t2/t2.nii.gz -l t2/labels.nii.gz -s t2/t2_seg.nii.gz ' \
'-param step=1,type=seg,algo=centermassrot,metric=MeanSquares:step=2,type=seg,algo=bsplinesyn,iter=5,metric=MeanSquares ' \
'-t template/ -r 0'
add_path_for_template = True # in this case, path to data should be added
parser = sct_register_to_template.get_parser()
dict_param = parser.parse(parameters.split(), check_file_exist=False)
if add_path_for_template:
dict_param_with_path = parser.add_path_to_file(deepcopy(dict_param),
path_data,
input_file=True)
else:
dict_param_with_path = parser.add_path_to_file(deepcopy(dict_param),
path_data,
input_file=True,
do_not_add_path=['-t'])
param_with_path = parser.dictionary_to_string(dict_param_with_path)
# Check if input files exist
if not (os.path.isfile(dict_param_with_path['-i'])
and os.path.isfile(dict_param_with_path['-l'])
and os.path.isfile(dict_param_with_path['-s'])):
status = 200
output = 'ERROR: the file(s) provided to test function do not exist in folder: ' + path_data
return status, output, DataFrame(data={
'status': int(status),
'output': output
},
index=[path_data])
# return status, output, DataFrame(
# data={'status': status, 'output': output,
# 'dice_template2anat': float('nan'), 'dice_anat2template': float('nan')},
# index=[path_data])
# if template is not specified, use default
# if not os.path.isdir(dict_param_with_path['-t']):
# status, path_sct = commands.getstatusoutput('echo $SCT_DIR')
# dict_param_with_path['-t'] = path_sct + default_template
# param_with_path = parser.dictionary_to_string(dict_param_with_path)
# get contrast folder from -i option.
# We suppose we can extract it as the first object when spliting with '/' delimiter.
contrast_folder = ''
input_filename = ''
if dict_param['-i'][0] == '/':
dict_param['-i'] = dict_param['-i'][1:]
input_split = dict_param['-i'].split('/')
if len(input_split) == 2:
contrast_folder = input_split[0] + '/'
input_filename = input_split[1]
else:
input_filename = input_split[0]
if not contrast_folder: # if no contrast folder, send error.
status = 201
output = 'ERROR: when extracting the contrast folder from input file in command line: ' + dict_param[
'-i'] + ' for ' + path_data
return status, output, DataFrame(data={
'status': int(status),
'output': output
},
index=[path_data])
# return status, output, DataFrame(
# data={'status': status, 'output': output, 'dice_template2anat': float('nan'), 'dice_anat2template': float('nan')}, index=[path_data])
# create output path
# TODO: create function for that
import time, random
subject_folder = path_data.split('/')
if subject_folder[-1] == '' and len(subject_folder) > 1:
subject_folder = subject_folder[-2]
else:
subject_folder = subject_folder[-1]
path_output = sct.slash_at_the_end(
'sct_register_to_template_' + subject_folder + '_' +
time.strftime("%y%m%d%H%M%S") + '_' + str(random.randint(1, 1000000)),
slash=1)
param_with_path += ' -ofolder ' + path_output
sct.create_folder(path_output)
# log file
# TODO: create function for that
import sys
fname_log = path_output + 'output.log'
sct.pause_stream_logger()
file_handler = sct.add_file_handler_to_logger(filename=fname_log,
mode='w',
log_format="%(message)s")
#
# stdout_log = file(fname_log, 'w')
# redirect to log file
# stdout_orig = sys.stdout
# sys.stdout = stdout_log
cmd = 'sct_register_to_template ' + param_with_path
output += '\n====================================================================================================\n' + cmd + '\n====================================================================================================\n\n' # copy command
time_start = time.time()
try:
status, o = sct.run(cmd, verbose)
except:
status, o = 1, 'ERROR: Function crashed!'
output += o
duration = time.time() - time_start
# if command ran without error, test integrity
if status == 0:
# get filename_template_seg
fname_template_seg = get_file_label(
sct.slash_at_the_end(dict_param_with_path['-t'], 1) + 'template/',
'spinal cord',
output='filewithpath')
# apply transformation to binary mask: template --> anat
sct.run(
'sct_apply_transfo -i ' + fname_template_seg + ' -d ' +
dict_param_with_path['-s'] + ' -w ' + path_output +
'warp_template2anat.nii.gz' + ' -o ' + path_output +
'test_template2anat.nii.gz -x nn', verbose)
# apply transformation to binary mask: anat --> template
sct.run(
'sct_apply_transfo -i ' + dict_param_with_path['-s'] + ' -d ' +
fname_template_seg + ' -w ' + path_output +
'warp_anat2template.nii.gz' + ' -o ' + path_output +
'test_anat2template.nii.gz -x nn', verbose)
# compute dice coefficient between template segmentation warped into anat and segmentation from anat
cmd = 'sct_dice_coefficient -i ' + dict_param_with_path[
'-s'] + ' -d ' + path_output + 'test_template2anat.nii.gz'
status1, output1 = sct.run(cmd, verbose)
# parse output and compare to acceptable threshold
dice_template2anat = float(
output1.split('3D Dice coefficient = ')[1].split('\n')[0])
if dice_template2anat < dice_threshold:
status1 = 99
# compute dice coefficient between segmentation from anat warped into template and template segmentation
# N.B. here we use -bmax because the FOV of the anat is smaller than the template
cmd = 'sct_dice_coefficient -i ' + fname_template_seg + ' -d ' + path_output + 'test_anat2template.nii.gz -bmax 1'
status2, output2 = sct.run(cmd, verbose)
# parse output and compare to acceptable threshold
dice_anat2template = float(
output2.split('3D Dice coefficient = ')[1].split('\n')[0])
if dice_anat2template < dice_threshold:
status2 = 99
# check if at least one integrity status was equal to 99
if status1 == 99 or status2 == 99:
status = 99
# concatenate outputs
output = output + output1 + output2
# transform results into Pandas structure
results = DataFrame(data={
'status': int(status),
'output': output,
'dice_template2anat': dice_template2anat,
'dice_anat2template': dice_anat2template,
'duration [s]': duration
},
index=[path_data])
sct.log.info(output)
sct.remove_handler(file_handler)
sct.start_stream_logger()
return status, output, results
def main():
parser = get_parser()
param = Param()
arguments = parser.parse(sys.argv[1:])
# get arguments
fname_data = arguments['-i']
fname_seg = arguments['-s']
fname_landmarks = arguments['-l']
if '-ofolder' in arguments:
path_output = arguments['-ofolder']
else:
path_output = ''
path_template = sct.slash_at_the_end(arguments['-t'], 1)
contrast_template = arguments['-c']
ref = arguments['-ref']
remove_temp_files = int(arguments['-r'])
verbose = int(arguments['-v'])
param.verbose = verbose # TODO: not clean, unify verbose or param.verbose in code, but not both
if '-param-straighten' in arguments:
param.param_straighten = arguments['-param-straighten']
# if '-cpu-nb' in arguments:
# arg_cpu = ' -cpu-nb '+str(arguments['-cpu-nb'])
# else:
# arg_cpu = ''
# registration parameters
if '-param' in arguments:
# reset parameters but keep step=0 (might be overwritten if user specified step=0)
paramreg = ParamregMultiStep([step0])
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# add user parameters
for paramStep in arguments['-param']:
paramreg.addStep(paramStep)
else:
paramreg = ParamregMultiStep([step0, step1, step2])
# if ref=subject, initialize registration using different affine parameters
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# initialize other parameters
# file_template_label = param.file_template_label
zsubsample = param.zsubsample
template = os.path.basename(os.path.normpath(path_template))
# smoothing_sigma = param.smoothing_sigma
# retrieve template file names
from sct_warp_template import get_file_label
file_template_vertebral_labeling = get_file_label(path_template+'template/', 'vertebral')
file_template = get_file_label(path_template+'template/', contrast_template.upper()+'-weighted')
file_template_seg = get_file_label(path_template+'template/', 'spinal cord')
# start timer
start_time = time.time()
# get fname of the template + template objects
fname_template = path_template+'template/'+file_template
fname_template_vertebral_labeling = path_template+'template/'+file_template_vertebral_labeling
fname_template_seg = path_template+'template/'+file_template_seg
# check file existence
# TODO: no need to do that!
sct.printv('\nCheck template files...')
sct.check_file_exist(fname_template, verbose)
sct.check_file_exist(fname_template_vertebral_labeling, verbose)
sct.check_file_exist(fname_template_seg, verbose)
# print arguments
sct.printv('\nCheck parameters:', verbose)
sct.printv(' Data: '+fname_data, verbose)
sct.printv(' Landmarks: '+fname_landmarks, verbose)
sct.printv(' Segmentation: '+fname_seg, verbose)
sct.printv(' Path template: '+path_template, verbose)
sct.printv(' Remove temp files: '+str(remove_temp_files), verbose)
# create QC folder
sct.create_folder(param.path_qc)
#
# sct.printv('\nParameters for registration:')
# for pStep in range(0, len(paramreg.steps)):
# sct.printv('Step #'+paramreg.steps[str(pStep)].step, verbose)
# sct.printv(' Type .................... '+paramreg.steps[str(pStep)].type, verbose)
# sct.printv(' Algorithm ............... '+paramreg.steps[str(pStep)].algo, verbose)
# sct.printv(' Metric .................. '+paramreg.steps[str(pStep)].metric, verbose)
# sct.printv(' Number of iterations .... '+paramreg.steps[str(pStep)].iter, verbose)
# sct.printv(' Shrink factor ........... '+paramreg.steps[str(pStep)].shrink, verbose)
# sct.printv(' Smoothing factor......... '+paramreg.steps[str(pStep)].smooth, verbose)
# sct.printv(' Gradient step ........... '+paramreg.steps[str(pStep)].gradStep, verbose)
# sct.printv(' Degree of polynomial .... '+paramreg.steps[str(pStep)].poly, verbose)
path_data, file_data, ext_data = sct.extract_fname(fname_data)
sct.printv('\nCheck if data, segmentation and landmarks are in the same space...')
if not sct.check_if_same_space(fname_data, fname_seg):
sct.printv('ERROR: Data image and segmentation are not in the same space. Please check space and orientation of your files', verbose, 'error')
if not sct.check_if_same_space(fname_data, fname_landmarks):
sct.printv('ERROR: Data image and landmarks are not in the same space. Please check space and orientation of your files', verbose, 'error')
sct.printv('\nCheck input labels...')
# check if label image contains coherent labels
image_label = Image(fname_landmarks)
# -> all labels must be different
labels = image_label.getNonZeroCoordinates(sorting='value')
hasDifferentLabels = True
for lab in labels:
for otherlabel in labels:
if lab != otherlabel and lab.hasEqualValue(otherlabel):
hasDifferentLabels = False
break
if not hasDifferentLabels:
sct.printv('ERROR: Wrong landmarks input. All labels must be different.', verbose, 'error')
# create temporary folder
path_tmp = sct.tmp_create(verbose=verbose)
# set temporary file names
ftmp_data = 'data.nii'
ftmp_seg = 'seg.nii.gz'
ftmp_label = 'label.nii.gz'
ftmp_template = 'template.nii'
ftmp_template_seg = 'template_seg.nii.gz'
ftmp_template_label = 'template_label.nii.gz'
# copy files to temporary folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
sct.run('sct_convert -i '+fname_data+' -o '+path_tmp+ftmp_data)
sct.run('sct_convert -i '+fname_seg+' -o '+path_tmp+ftmp_seg)
sct.run('sct_convert -i '+fname_landmarks+' -o '+path_tmp+ftmp_label)
sct.run('sct_convert -i '+fname_template+' -o '+path_tmp+ftmp_template)
sct.run('sct_convert -i '+fname_template_seg+' -o '+path_tmp+ftmp_template_seg)
# sct.run('sct_convert -i '+fname_template_label+' -o '+path_tmp+ftmp_template_label)
# go to tmp folder
os.chdir(path_tmp)
# Generate labels from template vertebral labeling
sct.printv('\nGenerate labels from template vertebral labeling', verbose)
sct.run('sct_label_utils -i '+fname_template_vertebral_labeling+' -vert-body 0 -o '+ftmp_template_label)
# check if provided labels are available in the template
sct.printv('\nCheck if provided labels are available in the template', verbose)
image_label_template = Image(ftmp_template_label)
labels_template = image_label_template.getNonZeroCoordinates(sorting='value')
if labels[-1].value > labels_template[-1].value:
sct.printv('ERROR: Wrong landmarks input. Labels must have correspondence in template space. \nLabel max '
'provided: ' + str(labels[-1].value) + '\nLabel max from template: ' +
str(labels_template[-1].value), verbose, 'error')
# binarize segmentation (in case it has values below 0 caused by manual editing)
sct.printv('\nBinarize segmentation', verbose)
sct.run('sct_maths -i seg.nii.gz -bin 0.5 -o seg.nii.gz')
# smooth segmentation (jcohenadad, issue #613)
# sct.printv('\nSmooth segmentation...', verbose)
# sct.run('sct_maths -i '+ftmp_seg+' -smooth 1.5 -o '+add_suffix(ftmp_seg, '_smooth'))
# jcohenadad: updated 2016-06-16: DO NOT smooth the seg anymore. Issue #
# sct.run('sct_maths -i '+ftmp_seg+' -smooth 0 -o '+add_suffix(ftmp_seg, '_smooth'))
# ftmp_seg = add_suffix(ftmp_seg, '_smooth')
# Switch between modes: subject->template or template->subject
if ref == 'template':
# resample data to 1mm isotropic
sct.printv('\nResample data to 1mm isotropic...', verbose)
sct.run('sct_resample -i '+ftmp_data+' -mm 1.0x1.0x1.0 -x linear -o '+add_suffix(ftmp_data, '_1mm'))
ftmp_data = add_suffix(ftmp_data, '_1mm')
sct.run('sct_resample -i '+ftmp_seg+' -mm 1.0x1.0x1.0 -x linear -o '+add_suffix(ftmp_seg, '_1mm'))
ftmp_seg = add_suffix(ftmp_seg, '_1mm')
# N.B. resampling of labels is more complicated, because they are single-point labels, therefore resampling with neighrest neighbour can make them disappear. Therefore a more clever approach is required.
resample_labels(ftmp_label, ftmp_data, add_suffix(ftmp_label, '_1mm'))
ftmp_label = add_suffix(ftmp_label, '_1mm')
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
sct.run('sct_image -i '+ftmp_data+' -setorient RPI -o '+add_suffix(ftmp_data, '_rpi'))
ftmp_data = add_suffix(ftmp_data, '_rpi')
sct.run('sct_image -i '+ftmp_seg+' -setorient RPI -o '+add_suffix(ftmp_seg, '_rpi'))
ftmp_seg = add_suffix(ftmp_seg, '_rpi')
sct.run('sct_image -i '+ftmp_label+' -setorient RPI -o '+add_suffix(ftmp_label, '_rpi'))
ftmp_label = add_suffix(ftmp_label, '_rpi')
# get landmarks in native space
# crop segmentation
# output: segmentation_rpi_crop.nii.gz
status_crop, output_crop = sct.run('sct_crop_image -i '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_crop')+' -dim 2 -bzmax', verbose)
ftmp_seg = add_suffix(ftmp_seg, '_crop')
cropping_slices = output_crop.split('Dimension 2: ')[1].split('\n')[0].split(' ')
# straighten segmentation
sct.printv('\nStraighten the spinal cord using centerline/segmentation...', verbose)
# 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 '+ftmp_seg+' -w warp_curve2straight.nii.gz -d straight_ref.nii.gz -o '+add_suffix(ftmp_seg, '_straight'))
else:
sct.run('sct_straighten_spinalcord -i '+ftmp_seg+' -s '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_straight')+' -qc 0 -r 0 -v '+str(verbose), verbose)
# N.B. DO NOT UPDATE VARIABLE ftmp_seg BECAUSE TEMPORARY USED LATER
# re-define warping field using non-cropped space (to avoid issue #367)
sct.run('sct_concat_transfo -w warp_straight2curve.nii.gz -d '+ftmp_data+' -o warp_straight2curve.nii.gz')
# Label preparation:
# --------------------------------------------------------------------------------
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run('sct_label_utils -i '+ftmp_template_label+' -o '+ftmp_template_label+' -remove '+ftmp_label)
# Dilating the input label so they can be straighten without losing them
sct.printv('\nDilating input labels using 3vox ball radius')
sct.run('sct_maths -i '+ftmp_label+' -o '+add_suffix(ftmp_label, '_dilate')+' -dilate 3')
ftmp_label = add_suffix(ftmp_label, '_dilate')
# Apply straightening to labels
sct.printv('\nApply straightening to labels...', verbose)
sct.run('sct_apply_transfo -i '+ftmp_label+' -o '+add_suffix(ftmp_label, '_straight')+' -d '+add_suffix(ftmp_seg, '_straight')+' -w warp_curve2straight.nii.gz -x nn')
ftmp_label = add_suffix(ftmp_label, '_straight')
# Compute rigid transformation straight landmarks --> template landmarks
sct.printv('\nEstimate transformation for step #0...', verbose)
from msct_register_landmarks import register_landmarks
try:
register_landmarks(ftmp_label, ftmp_template_label, paramreg.steps['0'].dof, fname_affine='straight2templateAffine.txt', verbose=verbose)
except Exception:
sct.printv('ERROR: input labels do not seem to be at the right place. Please check the position of the labels. See documentation for more details: https://sourceforge.net/p/spinalcordtoolbox/wiki/create_labels/', verbose=verbose, type='error')
# Concatenate transformations: curve --> straight --> affine
sct.printv('\nConcatenate transformations: curve --> straight --> affine...', verbose)
sct.run('sct_concat_transfo -w warp_curve2straight.nii.gz,straight2templateAffine.txt -d template.nii -o warp_curve2straightAffine.nii.gz')
# Apply transformation
sct.printv('\nApply transformation...', verbose)
sct.run('sct_apply_transfo -i '+ftmp_data+' -o '+add_suffix(ftmp_data, '_straightAffine')+' -d '+ftmp_template+' -w warp_curve2straightAffine.nii.gz')
ftmp_data = add_suffix(ftmp_data, '_straightAffine')
sct.run('sct_apply_transfo -i '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_straightAffine')+' -d '+ftmp_template+' -w warp_curve2straightAffine.nii.gz -x linear')
ftmp_seg = add_suffix(ftmp_seg, '_straightAffine')
"""
# Benjamin: Issue from Allan Martin, about the z=0 slice that is screwed up, caused by the affine transform.
# Solution found: remove slices below and above landmarks to avoid rotation effects
points_straight = []
for coord in landmark_template:
points_straight.append(coord.z)
min_point, max_point = int(round(np.min(points_straight))), int(round(np.max(points_straight)))
sct.run('sct_crop_image -i ' + ftmp_seg + ' -start ' + str(min_point) + ' -end ' + str(max_point) + ' -dim 2 -b 0 -o ' + add_suffix(ftmp_seg, '_black'))
ftmp_seg = add_suffix(ftmp_seg, '_black')
"""
# binarize
sct.printv('\nBinarize segmentation...', verbose)
sct.run('sct_maths -i '+ftmp_seg+' -bin 0.5 -o '+add_suffix(ftmp_seg, '_bin'))
ftmp_seg = add_suffix(ftmp_seg, '_bin')
# find min-max of anat2template (for subsequent cropping)
zmin_template, zmax_template = find_zmin_zmax(ftmp_seg)
# crop template in z-direction (for faster processing)
sct.printv('\nCrop data in template space (for faster processing)...', verbose)
sct.run('sct_crop_image -i '+ftmp_template+' -o '+add_suffix(ftmp_template, '_crop')+' -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
ftmp_template = add_suffix(ftmp_template, '_crop')
sct.run('sct_crop_image -i '+ftmp_template_seg+' -o '+add_suffix(ftmp_template_seg, '_crop')+' -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
ftmp_template_seg = add_suffix(ftmp_template_seg, '_crop')
sct.run('sct_crop_image -i '+ftmp_data+' -o '+add_suffix(ftmp_data, '_crop')+' -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
ftmp_data = add_suffix(ftmp_data, '_crop')
sct.run('sct_crop_image -i '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_crop')+' -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
ftmp_seg = add_suffix(ftmp_seg, '_crop')
# sub-sample in z-direction
sct.printv('\nSub-sample in z-direction (for faster processing)...', verbose)
sct.run('sct_resample -i '+ftmp_template+' -o '+add_suffix(ftmp_template, '_sub')+' -f 1x1x'+zsubsample, verbose)
ftmp_template = add_suffix(ftmp_template, '_sub')
sct.run('sct_resample -i '+ftmp_template_seg+' -o '+add_suffix(ftmp_template_seg, '_sub')+' -f 1x1x'+zsubsample, verbose)
ftmp_template_seg = add_suffix(ftmp_template_seg, '_sub')
sct.run('sct_resample -i '+ftmp_data+' -o '+add_suffix(ftmp_data, '_sub')+' -f 1x1x'+zsubsample, verbose)
ftmp_data = add_suffix(ftmp_data, '_sub')
sct.run('sct_resample -i '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_sub')+' -f 1x1x'+zsubsample, verbose)
ftmp_seg = add_suffix(ftmp_seg, '_sub')
# Registration straight spinal cord to template
sct.printv('\nRegister straight spinal cord to template...', verbose)
# loop across registration steps
warp_forward = []
warp_inverse = []
for i_step in range(1, len(paramreg.steps)):
sct.printv('\nEstimate transformation for step #'+str(i_step)+'...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_data
dest = ftmp_template
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_seg
dest = ftmp_template_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# if step>1, apply warp_forward_concat to the src image to be used
if i_step > 1:
# sct.run('sct_apply_transfo -i '+src+' -d '+dest+' -w '+','.join(warp_forward)+' -o '+sct.add_suffix(src, '_reg')+' -x '+interp_step, verbose)
# apply transformation from previous step, to use as new src for registration
sct.run('sct_apply_transfo -i '+src+' -d '+dest+' -w '+','.join(warp_forward)+' -o '+add_suffix(src, '_regStep'+str(i_step-1))+' -x '+interp_step, verbose)
src = add_suffix(src, '_regStep'+str(i_step-1))
# register src --> dest
# TODO: display param for debugging
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.append(warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: anat --> template...', verbose)
sct.run('sct_concat_transfo -w warp_curve2straightAffine.nii.gz,'+','.join(warp_forward)+' -d template.nii -o warp_anat2template.nii.gz', verbose)
# sct.run('sct_concat_transfo -w warp_curve2straight.nii.gz,straight2templateAffine.txt,'+','.join(warp_forward)+' -d template.nii -o warp_anat2template.nii.gz', verbose)
sct.printv('\nConcatenate transformations: template --> anat...', verbose)
warp_inverse.reverse()
sct.run('sct_concat_transfo -w '+','.join(warp_inverse)+',-straight2templateAffine.txt,warp_straight2curve.nii.gz -d data.nii -o warp_template2anat.nii.gz', verbose)
# register template->subject
elif ref == 'subject':
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
sct.run('sct_image -i ' + ftmp_data + ' -setorient RPI -o ' + add_suffix(ftmp_data, '_rpi'))
ftmp_data = add_suffix(ftmp_data, '_rpi')
sct.run('sct_image -i ' + ftmp_seg + ' -setorient RPI -o ' + add_suffix(ftmp_seg, '_rpi'))
ftmp_seg = add_suffix(ftmp_seg, '_rpi')
sct.run('sct_image -i ' + ftmp_label + ' -setorient RPI -o ' + add_suffix(ftmp_label, '_rpi'))
ftmp_label = add_suffix(ftmp_label, '_rpi')
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run('sct_label_utils -i '+ftmp_template_label+' -o '+ftmp_template_label+' -remove '+ftmp_label)
# Add one label because at least 3 orthogonal labels are required to estimate an affine transformation. This new label is added at the level of the upper most label (lowest value), at 1cm to the right.
for i_file in [ftmp_label, ftmp_template_label]:
im_label = Image(i_file)
coord_label = im_label.getCoordinatesAveragedByValue() # N.B. landmarks are sorted by value
# Create new label
from copy import deepcopy
new_label = deepcopy(coord_label[0])
# move it 5mm to the left (orientation is RAS)
nx, ny, nz, nt, px, py, pz, pt = im_label.dim
new_label.x = round(coord_label[0].x + 5.0 / px)
# assign value 99
new_label.value = 99
# Add to existing image
im_label.data[new_label.x, new_label.y, new_label.z] = new_label.value
# Overwrite label file
# im_label.setFileName('label_rpi_modif.nii.gz')
im_label.save()
# Bring template to subject space using landmark-based transformation
sct.printv('\nEstimate transformation for step #0...', verbose)
from msct_register_landmarks import register_landmarks
warp_forward = ['template2subjectAffine.txt']
warp_inverse = ['-template2subjectAffine.txt']
try:
register_landmarks(ftmp_template_label, ftmp_label, paramreg.steps['0'].dof, fname_affine=warp_forward[0], verbose=verbose, path_qc=param.path_qc)
except Exception:
sct.printv('ERROR: input labels do not seem to be at the right place. Please check the position of the labels. See documentation for more details: https://sourceforge.net/p/spinalcordtoolbox/wiki/create_labels/', verbose=verbose, type='error')
# loop across registration steps
for i_step in range(1, len(paramreg.steps)):
sct.printv('\nEstimate transformation for step #'+str(i_step)+'...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_template
dest = ftmp_data
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_template_seg
dest = ftmp_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# apply transformation from previous step, to use as new src for registration
sct.run('sct_apply_transfo -i '+src+' -d '+dest+' -w '+','.join(warp_forward)+' -o '+add_suffix(src, '_regStep'+str(i_step-1))+' -x '+interp_step, verbose)
src = add_suffix(src, '_regStep'+str(i_step-1))
# register src --> dest
# TODO: display param for debugging
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.insert(0, warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: template --> subject...', verbose)
sct.run('sct_concat_transfo -w '+','.join(warp_forward)+' -d data.nii -o warp_template2anat.nii.gz', verbose)
sct.printv('\nConcatenate transformations: subject --> template...', verbose)
sct.run('sct_concat_transfo -w '+','.join(warp_inverse)+' -d template.nii -o warp_anat2template.nii.gz', verbose)
# Apply warping fields to anat and template
sct.run('sct_apply_transfo -i template.nii -o template2anat.nii.gz -d data.nii -w warp_template2anat.nii.gz -crop 1', verbose)
sct.run('sct_apply_transfo -i data.nii -o anat2template.nii.gz -d template.nii -w warp_anat2template.nii.gz -crop 1', verbose)
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(path_tmp+'warp_template2anat.nii.gz', path_output+'warp_template2anat.nii.gz', verbose)
sct.generate_output_file(path_tmp+'warp_anat2template.nii.gz', path_output+'warp_anat2template.nii.gz', verbose)
sct.generate_output_file(path_tmp+'template2anat.nii.gz', path_output+'template2anat'+ext_data, verbose)
sct.generate_output_file(path_tmp+'anat2template.nii.gz', path_output+'anat2template'+ext_data, verbose)
if ref == 'template':
# copy straightening files in case subsequent SCT functions need them
sct.generate_output_file(path_tmp+'warp_curve2straight.nii.gz', path_output+'warp_curve2straight.nii.gz', verbose)
sct.generate_output_file(path_tmp+'warp_straight2curve.nii.gz', path_output+'warp_straight2curve.nii.gz', verbose)
sct.generate_output_file(path_tmp+'straight_ref.nii.gz', path_output+'straight_ref.nii.gz', verbose)
# Delete temporary files
if remove_temp_files:
sct.printv('\nDelete temporary files...', verbose)
sct.run('rm -rf '+path_tmp)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: '+str(int(round(elapsed_time)))+'s', verbose)
# to view results
sct.printv('\nTo view results, type:', verbose)
sct.printv('fslview '+fname_data+' '+path_output+'template2anat -b 0,4000 &', verbose, 'info')
sct.printv('fslview '+fname_template+' -b 0,5000 '+path_output+'anat2template &\n', verbose, 'info')
