def register_data(im_src, im_dest, param_reg, path_copy_warp=None, rm_tmp=True):
'''
Parameters
----------
im_src: class Image: source image
im_dest: class Image: destination image
param_reg: str: registration parameter
path_copy_warp: path: path to copy the warping fields
Returns: im_src_reg: class Image: source image registered on destination image
-------
'''
# im_src and im_dest are already preprocessed (in theory: im_dest = mean_image)
# binarize images to get seg
im_src_seg = binarize(im_src, thr_min=1, thr_max=1)
im_dest_seg = binarize(im_dest)
# create tmp dir and go in it
tmp_dir = tmp_create()
os.chdir(tmp_dir)
# save image and seg
fname_src = 'src.nii.gz'
im_src.setFileName(fname_src)
im_src.save()
fname_src_seg = 'src_seg.nii.gz'
im_src_seg.setFileName(fname_src_seg)
im_src_seg.save()
fname_dest = 'dest.nii.gz'
im_dest.setFileName(fname_dest)
im_dest.save()
fname_dest_seg = 'dest_seg.nii.gz'
im_dest_seg.setFileName(fname_dest_seg)
im_dest_seg.save()
# do registration using param_reg
sct_register_multimodal.main(args=['-i', fname_src,
'-d', fname_dest,
'-iseg', fname_src_seg,
'-dseg', fname_dest_seg,
'-param', param_reg])
# get registration result
fname_src_reg = add_suffix(fname_src, '_reg')
im_src_reg = Image(fname_src_reg)
# get out of tmp dir
os.chdir('..')
# copy warping fields
if path_copy_warp is not None and os.path.isdir(os.path.abspath(path_copy_warp)):
path_copy_warp = os.path.abspath(path_copy_warp)
file_src = extract_fname(fname_src)[1]
file_dest = extract_fname(fname_dest)[1]
fname_src2dest = 'warp_' + file_src +'2' + file_dest +'.nii.gz'
fname_dest2src = 'warp_' + file_dest +'2' + file_src +'.nii.gz'
shutil.copy(tmp_dir +'/' + fname_src2dest, path_copy_warp + '/')
shutil.copy(tmp_dir + '/' + fname_dest2src, path_copy_warp + '/')
if rm_tmp:
# remove tmp dir
shutil.rmtree(tmp_dir)
# return res image
return im_src_reg, fname_src2dest, fname_dest2src
def test_sequences(fake_3dimage_sct):
"""
Test correct behaviour in some Image manipulation sequences
"""
img = fake_3dimage_sct.copy()
path_tmp = sct.tmp_create(basename="test_sequences")
path_a = os.path.join(path_tmp, 'a.nii')
path_b = os.path.join(path_tmp, 'b.nii')
img.save(path_a)
assert img.absolutepath is None
img.save(path_b, mutable=True)
assert img._path is not None
assert img.absolutepath is not None
assert img.absolutepath == os.path.abspath(path_b)
img.save(path_a) \
.change_orientation("RPI") \
.save(path_b, mutable=True)
assert img.absolutepath is not None
assert img.absolutepath == os.path.abspath(path_b)
def apply_transfo(im_src, im_dest, warp, interp='spline', rm_tmp=True):
# create tmp dir and go in it
tmp_dir = tmp_create()
# copy warping field to tmp dir
shutil.copy(warp, tmp_dir)
warp = ''.join(extract_fname(warp)[1:])
# go to tmp dir
os.chdir(tmp_dir)
# save image and seg
fname_src = 'src.nii.gz'
im_src.setFileName(fname_src)
im_src.save()
fname_dest = 'dest.nii.gz'
im_dest.setFileName(fname_dest)
im_dest.save()
# apply warping field
fname_src_reg = add_suffix(fname_src, '_reg')
sct_apply_transfo.main(args=['-i', fname_src,
'-d', fname_dest,
'-w', warp,
'-x', interp])
im_src_reg = Image(fname_src_reg)
# get out of tmp dir
os.chdir('..')
if rm_tmp:
# remove tmp dir
shutil.rmtree(tmp_dir)
# return res image
return im_src_reg
def test_change_nd_orientation(fake_4dimage_sct):
import sct_image
im_src = fake_4dimage_sct.copy()
path_tmp = sct.tmp_create(basename="test_reorient")
im_src.save(os.path.join(path_tmp, "src.nii"), mutable=True)
print(im_src.orientation, im_src.data.shape)
def orient2shape(orient):
# test-data-specific thing
letter2dim = dict(
L=2,
R=2,
A=3,
P=3,
I=4,
S=4,
)
return tuple([letter2dim[x] for x in orient] + [5])
orientation = im_src.orientation
assert orientation == "LPI"
assert im_src.header.get_best_affine()[:3,3].tolist() == [0,0,0]
im_dst = msct_image.change_orientation(im_src, "RPI")
assert im_dst.orientation == "RPI"
assert im_dst.data.shape == orient2shape("RPI")
assert im_dst.header.get_best_affine()[:3,3].tolist() == [2-1,0,0]
def __init__(self, fname_im, contrast, fname_seg, path_out, verbose):
self.fname_im = fname_im
self.contrast = contrast
self.fname_seg = fname_seg
self.path_out = path_out
self.verbose = verbose
self.tmp_dir = sct.tmp_create(verbose=self.verbose) # path to tmp directory
self.orientation_im = Image(self.fname_im).orientation # to re-orient the data at the end
self.slice2D_im = sct.extract_fname(self.fname_im)[1] + '_midSag.nii' # file used to do the detection, with only one slice
self.dection_map_pmj = sct.extract_fname(self.fname_im)[1] + '_map_pmj' # file resulting from the detection
# path to the pmj detector
self.pmj_model = os.path.join(sct.__data_dir__, 'pmj_models', '{}_model'.format(self.contrast))
self.threshold = -0.75 if self.contrast == 't1' else 0.8 # detection map threshold, depends on the contrast
self.fname_out = sct.extract_fname(self.fname_im)[1] + '_pmj.nii.gz'
self.fname_qc = 'qc_pmj.png'
def __init__(self, param=None, param_glcm=None):
self.param = param if param is not None else Param()
self.param_glcm = param_glcm if param_glcm is not None else ParamGLCM()
# create tmp directory
self.tmp_dir = sct.tmp_create(verbose=self.param.verbose) # path to tmp directory
if self.param.dim == 'ax':
self.orientation_extraction = 'RPI'
elif self.param.dim == 'sag':
self.orientation_extraction = 'IPR'
else:
self.orientation_extraction = 'IRP'
# metric_lst=['property_distance_angle']
self.metric_lst = []
for m in list(itertools.product(self.param_glcm.feature.split(','), self.param_glcm.angle.split(','))):
text_name = m[0] if m[0].upper() != 'asm'.upper() else m[0].upper()
self.metric_lst.append(text_name + '_' + str(self.param_glcm.distance) + '_' + str(m[1]))
# dct_im_seg{'im': list_of_axial_slice, 'seg': list_of_axial_masked_slice}
self.dct_im_seg = {'im': None, 'seg': None}
# to re-orient the data at the end if needed
self.orientation_im = Image(self.param.fname_im).orientation
self.fname_metric_lst = {}
def __init__(self, fname_mask, fname_sc, fname_ref, path_template, path_ofolder, verbose):
self.fname_mask = fname_mask
self.fname_sc = fname_sc
self.fname_ref = fname_ref
self.path_template = path_template
self.path_ofolder = path_ofolder
self.verbose = verbose
self.wrk_dir = os.getcwd()
if not set(np.unique(Image(fname_mask).data)) == set([0.0, 1.0]):
if set(np.unique(Image(fname_mask).data)) == set([0.0]):
printv('WARNING: Empty masked image', self.verbose, 'warning')
else:
printv("ERROR input file %s is not binary file with 0 and 1 values" % fname_mask, 1, 'error')
# create tmp directory
self.tmp_dir = tmp_create(verbose=verbose) # path to tmp directory
# lesion file where each lesion has a different value
self.fname_label = extract_fname(self.fname_mask)[1] + '_label' + extract_fname(self.fname_mask)[2]
# initialization of measure sheet
measure_lst = ['label', 'volume [mm3]', 'length [mm]', 'max_equivalent_diameter [mm]']
if self.fname_ref is not None:
for measure in ['mean', 'std']:
measure_lst.append(measure + '_' + extract_fname(self.fname_ref)[1])
measure_dct = {}
for column in measure_lst:
measure_dct[column] = None
self.measure_pd = pd.DataFrame(data=measure_dct, index=range(0), columns=measure_lst)
# orientation of the input image
self.orientation = None
# volume object
self.volumes = None
# initialization of proportion measures, related to registrated atlas
if self.path_template is not None:
self.path_atlas = os.path.join(self.path_template, "atlas")
self.path_levels = os.path.join(self.path_template, "template", "PAM50_levels.nii.gz")
else:
self.path_atlas, self.path_levels = None, None
self.vert_lst = None
self.atlas_roi_lst = None
self.distrib_matrix_dct = {}
# output names
self.pickle_name = extract_fname(self.fname_mask)[1] + '_analyzis.pkl'
self.excel_name = extract_fname(self.fname_mask)[1] + '_analyzis.xls'
def register_slicewise(fname_src,
fname_dest,
fname_mask='',
warp_forward_out='step0Warp.nii.gz',
warp_inverse_out='step0InverseWarp.nii.gz',
paramreg=None,
ants_registration_params=None,
path_qc='./',
verbose=0):
# create temporary folder
path_tmp = sct.tmp_create(verbose)
# copy data to temp folder
sct.printv('\nCopy input data to temp folder...', verbose)
convert(fname_src, path_tmp+'src.nii')
convert(fname_dest, path_tmp+'dest.nii')
if fname_mask != '':
convert(fname_mask, path_tmp+'mask.nii.gz')
# go to temporary folder
chdir(path_tmp)
# Calculate displacement
if paramreg.algo == 'centermass':
# translation of center of mass between source and destination in voxel space
register2d_centermassrot('src.nii', 'dest.nii', fname_warp=warp_forward_out, fname_warp_inv=warp_inverse_out, rot=0, poly=int(paramreg.poly), path_qc=path_qc, verbose=verbose)
elif paramreg.algo == 'centermassrot':
# translation of center of mass and rotation based on source and destination first eigenvectors from PCA.
register2d_centermassrot('src.nii', 'dest.nii', fname_warp=warp_forward_out, fname_warp_inv=warp_inverse_out, rot=1, poly=int(paramreg.poly), path_qc=path_qc, verbose=verbose, pca_eigenratio_th=float(paramreg.pca_eigenratio_th))
elif paramreg.algo == 'columnwise':
# scaling R-L, then column-wise center of mass alignment and scaling
register2d_columnwise('src.nii', 'dest.nii', fname_warp=warp_forward_out, fname_warp_inv=warp_inverse_out, verbose=verbose, path_qc=path_qc, smoothWarpXY=int(paramreg.smoothWarpXY))
else:
# convert SCT flags into ANTs-compatible flags
algo_dic = {'translation': 'Translation', 'rigid': 'Rigid', 'affine': 'Affine', 'syn': 'SyN', 'bsplinesyn': 'BSplineSyN', 'centermass': 'centermass'}
paramreg.algo = algo_dic[paramreg.algo]
# run slicewise registration
register2d('src.nii', 'dest.nii', fname_mask=fname_mask, fname_warp=warp_forward_out, fname_warp_inv=warp_inverse_out, paramreg=paramreg, ants_registration_params=ants_registration_params, verbose=verbose)
sct.printv('\nMove warping fields to parent folder...', verbose)
sct.run('mv '+warp_forward_out+' ../')
sct.run('mv '+warp_inverse_out+' ../')
# go back to parent folder
chdir('../')
def __init__(self, param_seg=None, param_model=None, param_data=None, param=None):
self.param_seg = param_seg if param_seg is not None else ParamSeg()
self.param_model = param_model if param_model is not None else ParamModel()
self.param_data = param_data if param_data is not None else ParamData()
self.param = param if param is not None else Param()
# create model:
self.model = Model(param_model=self.param_model, param_data=self.param_data, param=self.param)
# create tmp directory
self.tmp_dir = tmp_create(verbose=self.param.verbose) # path to tmp directory
self.target_im = None # list of slices
self.info_preprocessing = None # dic containing {'orientation': 'xxx', 'im_sc_seg_rpi': im, 'interpolated_images': [list of im = interpolated image data per slice]}
self.projected_target = None # list of coordinates of the target slices in the model reduced space
self.im_res_gmseg = None
self.im_res_wmseg = None
def visualize_warp(fname_warp, fname_grid=None, step=3, rm_tmp=True):
if fname_grid is None:
from numpy import zeros
tmp_dir = sct.tmp_create()
im_warp = Image(fname_warp)
os.chdir(tmp_dir)
assert len(im_warp.data.shape) == 5, "ERROR: Warping field does bot have 5 dimensions..."
nx, ny, nz, nt, ndimwarp = im_warp.data.shape
# nx, ny, nz, nt, px, py, pz, pt = im_warp.dim
# This does not work because dimensions of a warping field are not correctly read : it would be 1,1,1,1,1,1,1,1
sq = zeros((step, step))
sq[step - 1] = 1
sq[:, step - 1] = 1
dat = zeros((nx, ny, nz))
for i in range(0, dat.shape[0], step):
for j in range(0, dat.shape[1], step):
for k in range(dat.shape[2]):
if dat[i : i + step, j : j + step, k].shape == (step, step):
dat[i : i + step, j : j + step, k] = sq
fname_grid = "grid_" + str(step) + ".nii.gz"
im_grid = Image(param=dat)
grid_hdr = im_warp.hdr
im_grid.hdr = grid_hdr
im_grid.setFileName(fname_grid)
im_grid.save()
fname_grid_resample = sct.add_suffix(fname_grid, "_resample")
sct.run("sct_resample -i " + fname_grid + " -f 3x3x1 -x nn -o " + fname_grid_resample)
fname_grid = tmp_dir + fname_grid_resample
os.chdir("..")
path_warp, file_warp, ext_warp = sct.extract_fname(fname_warp)
grid_warped = path_warp + "grid_warped_gm" + ext_warp
sct.run("sct_apply_transfo -i " + fname_grid + " -d " + fname_grid + " -w " + fname_warp + " -o " + grid_warped)
if rm_tmp:
sct.run("rm -rf " + tmp_dir, error_exit="warning")
return grid_warped
def func_rescale_header(fname_data, rescale_factor, verbose=0):
"""
Rescale the voxel dimension by modifying the NIFTI header qform. Write the output file in a temp folder.
:param fname_data:
:param rescale_factor:
:return: fname_data_rescaled
"""
import nibabel as nib
img = nib.load(fname_data)
# get qform
qform = img.header.get_qform()
# multiply by scaling factor
qform[0:3, 0:3] *= rescale_factor
# generate a new nifti file
header_rescaled = img.header.copy()
header_rescaled.set_qform(qform)
# the data are the same-- only the header changes
img_rescaled = nib.nifti1.Nifti1Image(img.get_data(), None, header=header_rescaled)
path_tmp = sct.tmp_create(basename="propseg", verbose=verbose)
fname_data_rescaled = os.path.join(path_tmp, os.path.basename(sct.add_suffix(fname_data, "_rescaled")))
nib.save(img_rescaled, fname_data_rescaled)
return fname_data_rescaled
def visualize_warp(fname_warp, fname_grid=None, step=3, rm_tmp=True):
if fname_grid is None:
from numpy import zeros
tmp_dir = tmp_create()
im_warp = Image(fname_warp)
status, out = run('fslhd '+fname_warp)
from os import chdir
chdir(tmp_dir)
dim1 = 'dim1 '
dim2 = 'dim2 '
dim3 = 'dim3 '
nx = int(out[out.find(dim1):][len(dim1):out[out.find(dim1):].find('\n')])
ny = int(out[out.find(dim2):][len(dim2):out[out.find(dim2):].find('\n')])
nz = int(out[out.find(dim3):][len(dim3):out[out.find(dim3):].find('\n')])
sq = zeros((step, step))
sq[step-1] = 1
sq[:, step-1] = 1
dat = zeros((nx, ny, nz))
for i in range(0, dat.shape[0], step):
for j in range(0, dat.shape[1], step):
for k in range(dat.shape[2]):
if dat[i:i+step, j:j+step, k].shape == (step, step):
dat[i:i+step, j:j+step, k] = sq
fname_grid = 'grid_'+str(step)+'.nii.gz'
im_grid = Image(param=dat)
grid_hdr = im_warp.hdr
im_grid.hdr = grid_hdr
im_grid.setFileName(fname_grid)
im_grid.save()
fname_grid_resample = add_suffix(fname_grid, '_resample')
run('sct_resample -i '+fname_grid+' -f 3x3x1 -x nn -o '+fname_grid_resample)
fname_grid = tmp_dir+fname_grid_resample
chdir('..')
path_warp, file_warp, ext_warp = extract_fname(fname_warp)
grid_warped = path_warp+extract_fname(fname_grid)[1]+'_'+file_warp+ext_warp
run('sct_apply_transfo -i '+fname_grid+' -d '+fname_grid+' -w '+fname_warp+' -o '+grid_warped)
if rm_tmp:
run('rm -rf '+tmp_dir, error_exit='warning')
def visualize_warp(fname_warp, fname_grid=None, step=3, rm_tmp=True):
if fname_grid is None:
from numpy import zeros
tmp_dir = sct.tmp_create()
im_warp = Image(fname_warp)
status, out = sct.run(['fslhd', fname_warp])
curdir = os.getcwd()
os.chdir(tmp_dir)
dim1 = 'dim1 '
dim2 = 'dim2 '
dim3 = 'dim3 '
nx = int(out[out.find(dim1):][len(dim1):out[out.find(dim1):].find('\n')])
ny = int(out[out.find(dim2):][len(dim2):out[out.find(dim2):].find('\n')])
nz = int(out[out.find(dim3):][len(dim3):out[out.find(dim3):].find('\n')])
sq = zeros((step, step))
sq[step - 1] = 1
sq[:, step - 1] = 1
dat = zeros((nx, ny, nz))
for i in range(0, dat.shape[0], step):
for j in range(0, dat.shape[1], step):
for k in range(dat.shape[2]):
if dat[i:i + step, j:j + step, k].shape == (step, step):
dat[i:i + step, j:j + step, k] = sq
fname_grid = 'grid_' + str(step) + '.nii.gz'
im_grid = Image(param=dat)
grid_hdr = im_warp.hdr
im_grid.hdr = grid_hdr
im_grid.absolutepath = fname_grid
im_grid.save()
fname_grid_resample = sct.add_suffix(fname_grid, '_resample')
sct.run(['sct_resample', '-i', fname_grid, '-f', '3x3x1', '-x', 'nn', '-o', fname_grid_resample])
fname_grid = os.path.join(tmp_dir, fname_grid_resample)
os.chdir(curdir)
path_warp, file_warp, ext_warp = sct.extract_fname(fname_warp)
grid_warped = os.path.join(path_warp, sct.extract_fname(fname_grid)[1] + '_' + file_warp + ext_warp)
sct.run(['sct_apply_transfo', '-i', fname_grid, '-d', fname_grid, '-w', fname_warp, '-o', grid_warped])
if rm_tmp:
sct.rmtree(tmp_dir)
def test_change_shape(fake_3dimage_sct):
# Add dimension
im_src = fake_3dimage_sct
shape = tuple(list(im_src.data.shape) + [1])
im_dst = msct_image.change_shape(im_src, shape)
path_tmp = sct.tmp_create(basename="test_reshape")
src_path = os.path.join(path_tmp, "src.nii")
dst_path = os.path.join(path_tmp, "dst.nii")
im_src.save(src_path)
im_dst.save(dst_path)
im_src = msct_image.Image(src_path)
im_dst = msct_image.Image(dst_path)
assert im_dst.data.shape == shape
data_src = im_src.data
data_dst = im_dst.data
assert (data_dst.reshape(data_src.shape) == data_src).all()
# Remove dimension
im_dst = im_dst.change_shape(im_src.data.shape)
assert im_dst.data.shape == im_src.data.shape
def main(args=None):
if args is None:
args = sys.argv[1:]
# initialize parameters
param = Param()
# Initialization
fname_output = ''
path_out = ''
fname_src_seg = ''
fname_dest_seg = ''
fname_src_label = ''
fname_dest_label = ''
generate_warpinv = 1
start_time = time.time()
# get path of the toolbox
status, path_sct = commands.getstatusoutput('echo $SCT_DIR')
# get default registration parameters
# step1 = Paramreg(step='1', type='im', algo='syn', metric='MI', iter='5', shrink='1', smooth='0', gradStep='0.5')
step0 = Paramreg(step='0', type='im', algo='syn', metric='MI', iter='0', shrink='1', smooth='0', gradStep='0.5', slicewise='0', dof='Tx_Ty_Tz_Rx_Ry_Rz') # only used to put src into dest space
step1 = Paramreg(step='1', type='im')
paramreg = ParamregMultiStep([step0, step1])
parser = get_parser(paramreg=paramreg)
arguments = parser.parse(args)
# get arguments
fname_src = arguments['-i']
fname_dest = arguments['-d']
if '-iseg' in arguments:
fname_src_seg = arguments['-iseg']
if '-dseg' in arguments:
fname_dest_seg = arguments['-dseg']
if '-ilabel' in arguments:
fname_src_label = arguments['-ilabel']
if '-dlabel' in arguments:
fname_dest_label = arguments['-dlabel']
if '-o' in arguments:
fname_output = arguments['-o']
if '-ofolder' in arguments:
path_out = arguments['-ofolder']
if '-owarp' in arguments:
fname_output_warp = arguments['-owarp']
else:
fname_output_warp = ''
if '-initwarp' in arguments:
fname_initwarp = os.path.abspath(arguments['-initwarp'])
else:
fname_initwarp = ''
if '-initwarpinv' in arguments:
fname_initwarpinv = os.path.abspath(arguments['-initwarpinv'])
else:
fname_initwarpinv = ''
if '-m' in arguments:
fname_mask = arguments['-m']
else:
fname_mask = ''
padding = arguments['-z']
if "-param" in arguments:
paramreg_user = arguments['-param']
# update registration parameters
for paramStep in paramreg_user:
paramreg.addStep(paramStep)
identity = int(arguments['-identity'])
interp = arguments['-x']
remove_temp_files = int(arguments['-r'])
verbose = int(arguments['-v'])
# sct.printv(arguments)
sct.printv('\nInput parameters:')
sct.printv(' Source .............. ' + fname_src)
sct.printv(' Destination ......... ' + fname_dest)
sct.printv(' Init transfo ........ ' + fname_initwarp)
sct.printv(' Mask ................ ' + fname_mask)
sct.printv(' Output name ......... ' + fname_output)
# sct.printv(' Algorithm ........... '+paramreg.algo)
# sct.printv(' Number of iterations '+paramreg.iter)
# sct.printv(' Metric .............. '+paramreg.metric)
sct.printv(' Remove temp files ... ' + str(remove_temp_files))
sct.printv(' Verbose ............. ' + str(verbose))
# update param
param.verbose = verbose
param.padding = padding
param.fname_mask = fname_mask
param.remove_temp_files = remove_temp_files
# Get if input is 3D
sct.printv('\nCheck if input data are 3D...', verbose)
sct.check_if_3d(fname_src)
sct.check_if_3d(fname_dest)
# Check if user selected type=seg, but did not input segmentation data
if 'paramreg_user' in locals():
if True in ['type=seg' in paramreg_user[i] for i in range(len(paramreg_user))]:
if fname_src_seg == '' or fname_dest_seg == '':
sct.printv('\nERROR: if you select type=seg you must specify -iseg and -dseg flags.\n', 1, 'error')
# Extract path, file and extension
path_src, file_src, ext_src = sct.extract_fname(fname_src)
path_dest, file_dest, ext_dest = sct.extract_fname(fname_dest)
# check if source and destination images have the same name (related to issue #373)
# If so, change names to avoid conflict of result files and warns the user
suffix_src, suffix_dest = '_reg', '_reg'
if file_src == file_dest:
suffix_src, suffix_dest = '_src_reg', '_dest_reg'
# define output folder and file name
if fname_output == '':
path_out = '' if not path_out else path_out # output in user's current directory
file_out = file_src + suffix_src
file_out_inv = file_dest + suffix_dest
ext_out = ext_src
else:
path, file_out, ext_out = sct.extract_fname(fname_output)
path_out = path if not path_out else path_out
file_out_inv = file_out + '_inv'
# create QC folder
sct.create_folder(param.path_qc)
# create temporary folder
path_tmp = sct.tmp_create()
# copy files to temporary folder
from sct_convert import convert
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
convert(fname_src, path_tmp + 'src.nii')
convert(fname_dest, path_tmp + 'dest.nii')
if fname_src_seg:
convert(fname_src_seg, path_tmp + 'src_seg.nii')
convert(fname_dest_seg, path_tmp + 'dest_seg.nii')
if fname_src_label:
convert(fname_src_label, path_tmp + 'src_label.nii')
convert(fname_dest_label, path_tmp + 'dest_label.nii')
if fname_mask != '':
convert(fname_mask, path_tmp + 'mask.nii.gz')
# go to tmp folder
os.chdir(path_tmp)
# reorient destination to RPI
sct.run('sct_image -i dest.nii -setorient RPI -o dest_RPI.nii')
if fname_dest_seg:
sct.run('sct_image -i dest_seg.nii -setorient RPI -o dest_seg_RPI.nii')
if fname_dest_label:
sct.run('sct_image -i dest_label.nii -setorient RPI -o dest_label_RPI.nii')
if identity:
# overwrite paramreg and only do one identity transformation
step0 = Paramreg(step='0', type='im', algo='syn', metric='MI', iter='0', shrink='1', smooth='0', gradStep='0.5')
paramreg = ParamregMultiStep([step0])
# Put source into destination space using header (no estimation -- purely based on header)
# TODO: Check if necessary to do that
# TODO: use that as step=0
# sct.printv('\nPut source into destination space using header...', verbose)
# sct.run('isct_antsRegistration -d 3 -t Translation[0] -m MI[dest_pad.nii,src.nii,1,16] -c 0 -f 1 -s 0 -o [regAffine,src_regAffine.nii] -n BSpline[3]', verbose)
# if segmentation, also do it for seg
# initialize list of warping fields
warp_forward = []
warp_inverse = []
# initial warping is specified, update list of warping fields and skip step=0
if fname_initwarp:
sct.printv('\nSkip step=0 and replace with initial transformations: ', param.verbose)
sct.printv(' ' + fname_initwarp, param.verbose)
# sct.run('cp '+fname_initwarp+' warp_forward_0.nii.gz', verbose)
warp_forward = [fname_initwarp]
start_step = 1
if fname_initwarpinv:
warp_inverse = [fname_initwarpinv]
else:
sct.printv('\nWARNING: No initial inverse warping field was specified, therefore the inverse warping field will NOT be generated.', param.verbose, 'warning')
generate_warpinv = 0
else:
start_step = 0
# loop across registration steps
for i_step in range(start_step, len(paramreg.steps)):
sct.printv('\n--\nESTIMATE TRANSFORMATION FOR STEP #' + str(i_step), param.verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = 'src.nii'
dest = 'dest_RPI.nii'
interp_step = 'spline'
elif paramreg.steps[str(i_step)].type == 'seg':
src = 'src_seg.nii'
dest = 'dest_seg_RPI.nii'
interp_step = 'nn'
elif paramreg.steps[str(i_step)].type == 'label':
src = 'src_label.nii'
dest = 'dest_label_RPI.nii'
interp_step = 'nn'
else:
# src = dest = interp_step = None
sct.printv('ERROR: Wrong image type.', 1, 'error')
# if step>0, apply warp_forward_concat to the src image to be used
if i_step > 0:
sct.printv('\nApply transformation from previous step', param.verbose)
sct.run('sct_apply_transfo -i ' + src + ' -d ' + dest + ' -w ' + ','.join(warp_forward) + ' -o ' + sct.add_suffix(src, '_reg') + ' -x ' + interp_step, verbose)
src = sct.add_suffix(src, '_reg')
# register src --> dest
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.insert(0, warp_inverse_out)
# Concatenate transformations
sct.printv('\nConcatenate transformations...', verbose)
sct.run('sct_concat_transfo -w ' + ','.join(warp_forward) + ' -d dest.nii -o warp_src2dest.nii.gz', verbose)
sct.run('sct_concat_transfo -w ' + ','.join(warp_inverse) + ' -d src.nii -o warp_dest2src.nii.gz', verbose)
# Apply warping field to src data
sct.printv('\nApply transfo source --> dest...', verbose)
sct.run('sct_apply_transfo -i src.nii -o src_reg.nii -d dest.nii -w warp_src2dest.nii.gz -x ' + interp, verbose)
sct.printv('\nApply transfo dest --> source...', verbose)
sct.run('sct_apply_transfo -i dest.nii -o dest_reg.nii -d src.nii -w warp_dest2src.nii.gz -x ' + interp, verbose)
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', verbose)
# generate: src_reg
fname_src2dest = sct.generate_output_file(path_tmp + 'src_reg.nii', path_out + file_out + ext_out, verbose)
# generate: forward warping field
if fname_output_warp == '':
fname_output_warp = path_out + 'warp_' + file_src + '2' + file_dest + '.nii.gz'
sct.generate_output_file(path_tmp + 'warp_src2dest.nii.gz', fname_output_warp, verbose)
if generate_warpinv:
# generate: dest_reg
fname_dest2src = sct.generate_output_file(path_tmp + 'dest_reg.nii', path_out + file_out_inv + ext_dest, verbose)
# generate: inverse warping field
sct.generate_output_file(path_tmp + 'warp_dest2src.nii.gz', path_out + 'warp_' + file_dest + '2' + file_src + '.nii.gz', verbose)
# Delete temporary files
if remove_temp_files:
sct.printv('\nRemove temporary files...', verbose)
sct.run('rm -rf ' + path_tmp, verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(round(elapsed_time))) + 's', verbose)
sct.printv('\nTo view results, type:', verbose)
if generate_warpinv:
sct.printv('fslview ' + fname_src + ' ' + fname_dest2src + ' &', verbose, 'info')
sct.printv('fslview ' + fname_dest + ' ' + fname_src2dest + ' &\n', verbose, 'info')
def check_and_correct_segmentation(fname_segmentation,
fname_centerline,
folder_output='',
threshold_distance=5.0,
remove_temp_files=1,
verbose=0):
"""
This function takes the outputs of isct_propseg (centerline and segmentation) and check if the centerline of the
segmentation is coherent with the centerline provided by the isct_propseg, especially on the edges (related
to issue #1074).
Args:
fname_segmentation: filename of binary segmentation
fname_centerline: filename of binary centerline
threshold_distance: threshold, in mm, beyond which centerlines are not coherent
verbose:
Returns: None
"""
sct.printv('\nCheck consistency of segmentation...', verbose)
# creating a temporary folder in which all temporary files will be placed and deleted afterwards
path_tmp = sct.tmp_create(basename="propseg", verbose=verbose)
from sct_convert import convert
convert(fname_segmentation,
os.path.join(path_tmp, "tmp.segmentation.nii.gz"),
verbose=0)
convert(fname_centerline,
os.path.join(path_tmp, "tmp.centerline.nii.gz"),
verbose=0)
fname_seg_absolute = os.path.abspath(fname_segmentation)
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# convert segmentation image to RPI
im_input = Image('tmp.segmentation.nii.gz')
image_input_orientation = im_input.orientation
sct_image.main(
"-i tmp.segmentation.nii.gz -setorient RPI -o tmp.segmentation_RPI.nii.gz -v 0"
.split())
sct_image.main(
"-i tmp.centerline.nii.gz -setorient RPI -o tmp.centerline_RPI.nii.gz -v 0"
.split())
# go through segmentation image, and compare with centerline from propseg
im_seg = Image('tmp.segmentation_RPI.nii.gz')
im_centerline = Image('tmp.centerline_RPI.nii.gz')
# Get size of data
sct.printv('\nGet data dimensions...', verbose)
nx, ny, nz, nt, px, py, pz, pt = im_seg.dim
# extraction of centerline provided by isct_propseg and computation of center of mass for each slice
# the centerline is defined as the center of the tubular mesh outputed by propseg.
centerline, key_centerline = {}, []
for i in range(nz):
slice = im_centerline.data[:, :, i]
if np.any(slice):
x_centerline, y_centerline = ndi.measurements.center_of_mass(slice)
centerline[str(i)] = [x_centerline, y_centerline]
key_centerline.append(i)
minz_centerline = np.min(key_centerline)
maxz_centerline = np.max(key_centerline)
mid_slice = int((maxz_centerline - minz_centerline) / 2)
# for each slice of the segmentation, check if only one object is present. If not, remove the slice from segmentation.
# If only one object (the spinal cord) is present in the slice, check if its center of mass is close to the centerline of isct_propseg.
slices_to_remove = [
False
] * nz # flag that decides if the slice must be removed
for i in range(minz_centerline, maxz_centerline + 1):
# extraction of slice
slice = im_seg.data[:, :, i]
distance = -1
label_objects, nb_labels = ndi.label(
slice) # count binary objects in the slice
if nb_labels > 1: # if there is more that one object in the slice, the slice is removed from the segmentation
slices_to_remove[i] = True
elif nb_labels == 1: # check if the centerline is coherent with the one from isct_propseg
x_centerline, y_centerline = ndi.measurements.center_of_mass(slice)
slice_nearest_coord = min(key_centerline, key=lambda x: abs(x - i))
coord_nearest_coord = centerline[str(slice_nearest_coord)]
distance = np.sqrt((
(x_centerline - coord_nearest_coord[0]) * px)**2 + (
(y_centerline - coord_nearest_coord[1]) * py)**2 +
((i - slice_nearest_coord) * pz)**2)
if distance >= threshold_distance: # threshold must be adjusted, default is 5 mm
slices_to_remove[i] = True
# Check list of removal and keep one continuous centerline (improve this comment)
# Method:
# starting from mid-centerline (in both directions), the first True encountered is applied to all following slices
slice_to_change = False
for i in range(mid_slice, nz):
if slice_to_change:
slices_to_remove[i] = True
elif slices_to_remove[i]:
slice_to_change = True
slice_to_change = False
for i in range(mid_slice, 0, -1):
if slice_to_change:
slices_to_remove[i] = True
elif slices_to_remove[i]:
slice_to_change = True
for i in range(0, nz):
# remove the slice
if slices_to_remove[i]:
im_seg.data[:, :, i] *= 0
# saving the image
im_seg.save('tmp.segmentation_RPI_c.nii.gz')
# replacing old segmentation with the corrected one
sct_image.main(
'-i tmp.segmentation_RPI_c.nii.gz -setorient {} -o {} -v 0'.format(
image_input_orientation, fname_seg_absolute).split())
os.chdir(curdir)
# display information about how much of the segmentation has been corrected
# remove temporary files
if remove_temp_files:
# sct.printv("\nRemove temporary files...", verbose)
sct.rmtree(path_tmp)
return ori
elif set:
# set orientation
printv('\nChange orientation...', verbose)
im_out = set_orientation(im, ori)
elif set_data:
im_out = set_orientation(im, ori, True)
else:
im_out = None
else:
from os import chdir
# 4D data: split along T dimension
# or 5D data: split along 5th dimension
# Create a temporary directory and go in it
tmp_folder = tmp_create(verbose)
chdir(tmp_folder)
if len(im.data.shape) == 5 and im.data.shape[-1] not in [0, 1]:
# 5D data
printv('\nSplit along 5th dimension...', verbose)
im_split_list = multicomponent_split(im)
dim = 5
else:
# 4D data
printv('\nSplit along T dimension...', verbose)
im_split_list = split_data(im, 3)
dim = 4
for im_s in im_split_list:
im_s.save(verbose=verbose)
if get:
def main():
# Initialization
size_data = 61
size_label = 1 # put zero for labels that are single points.
dice_acceptable = 0.39 # computed DICE should be 0.931034
test_passed = 0
remove_temp_files = 1
verbose = 1
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'hvr:')
except getopt.GetoptError:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-v'):
verbose = int(arg)
elif opt in ('-r'):
remove_temp_files = int(arg)
path_tmp = sct.tmp_create(basename="test_ants", verbose=verbose)
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Initialise numpy volumes
data_src = np.zeros((size_data, size_data, size_data), dtype=np.int16)
data_dest = np.zeros((size_data, size_data, size_data), dtype=np.int16)
# add labels for src image (curved).
# Labels can be big (more than single point), because when applying NN interpolation, single points might disappear
data_src[20 - size_label:20 + size_label + 1,
20 - size_label:20 + size_label + 1,
10 - size_label:10 + size_label + 1] = 1
data_src[30 - size_label:30 + size_label + 1,
30 - size_label:30 + size_label + 1,
30 - size_label:30 + size_label + 1] = 2
data_src[20 - size_label:20 + size_label + 1,
20 - size_label:20 + size_label + 1,
50 - size_label:50 + size_label + 1] = 3
# add labels for dest image (straight).
# Here, no need for big labels (bigger than single point) because these labels will not be re-interpolated.
data_dest[30 - size_label:30 + size_label + 1,
30 - size_label:30 + size_label + 1,
10 - size_label:10 + size_label + 1] = 1
data_dest[30 - size_label:30 + size_label + 1,
30 - size_label:30 + size_label + 1,
30 - size_label:30 + size_label + 1] = 2
data_dest[30 - size_label:30 + size_label + 1,
30 - size_label:30 + size_label + 1,
50 - size_label:50 + size_label + 1] = 3
# save as nifti
img_src = nib.Nifti1Image(data_src, np.eye(4))
nib.save(img_src, 'data_src.nii.gz')
img_dest = nib.Nifti1Image(data_dest, np.eye(4))
nib.save(img_dest, 'data_dest.nii.gz')
# Estimate rigid transformation
sct.printv('\nEstimate rigid transformation between paired landmarks...',
verbose)
# TODO fixup isct_ants* parsers
sct.run([
'isct_antsRegistration', '-d', '3', '-t', 'syn[1,3,1]', '-m',
'MeanSquares[data_dest.nii.gz,data_src.nii.gz,1,3]', '-f', '2', '-s',
'0', '-o', '[src2reg,data_src_reg.nii.gz]', '-c', '5', '-v', '1', '-n',
'NearestNeighbor'
],
verbose,
is_sct_binary=True)
# # Apply rigid transformation
# sct.printv('\nApply rigid transformation to curved landmarks...', verbose)
# sct.run('sct_apply_transfo -i data_src.nii.gz -o data_src_rigid.nii.gz -d data_dest.nii.gz -w curve2straight_rigid.txt -p nn', verbose)
#
# # Estimate b-spline transformation curve --> straight
# sct.printv('\nEstimate b-spline transformation: curve --> straight...', verbose)
# sct.run('isct_ANTSLandmarksBSplineTransform data_dest.nii.gz data_src_rigid.nii.gz warp_curve2straight_intermediate.nii.gz 5x5x5 3 2 0', verbose)
#
# # Concatenate rigid and non-linear transformations...
# sct.printv('\nConcatenate rigid and non-linear transformations...', verbose)
# cmd = 'isct_ComposeMultiTransform 3 warp_curve2straight.nii.gz -R data_dest.nii.gz warp_curve2straight_intermediate.nii.gz curve2straight_rigid.txt'
# sct.printv('>> '+cmd, verbose)
# sct.run(cmd)
#
# # Apply deformation to input image
# sct.printv('\nApply transformation to input image...', verbose)
# sct.run('sct_apply_transfo -i data_src.nii.gz -o data_src_warp.nii.gz -d data_dest.nii.gz -w warp_curve2straight.nii.gz -p nn', verbose)
#
# Compute DICE coefficient between src and dest
sct.printv('\nCompute DICE coefficient...', verbose)
sct.run([
"sct_dice_coefficient", "-i", "data_dest.nii.gz", "-d",
"data_src_reg.nii.gz", "-o", "dice.txt"
], verbose)
with open("dice.txt", "r") as file_dice:
dice = float(file_dice.read().replace('3D Dice coefficient = ', ''))
sct.printv(
'Dice coeff = ' + str(dice) + ' (should be above ' +
str(dice_acceptable) + ')', verbose)
# Check if DICE coefficient is above acceptable value
if dice > dice_acceptable:
test_passed = 1
# come back
os.chdir(curdir)
# Delete temporary files
if remove_temp_files == 1:
sct.printv('\nDelete temporary files...', verbose)
sct.rmtree(path_tmp)
# output result for parent function
if test_passed:
sct.printv('\nTest passed!\n', verbose)
sys.exit(0)
else:
sct.printv('\nTest failed!\n', verbose)
sys.exit(1)
def main(args=None):
import numpy as np
import spinalcordtoolbox.image as msct_image
# Initialization
fname_mt0 = ''
fname_mt1 = ''
file_out = param.file_out
# register = param.register
# remove_temp_files = param.remove_temp_files
# verbose = param.verbose
# check user arguments
if not args:
args = sys.argv[1:]
# Check input parameters
parser = get_parser()
arguments = parser.parse(args)
fname_mt0 = arguments['-mt0']
fname_mt1 = arguments['-mt1']
remove_temp_files = int(arguments['-r'])
verbose = int(arguments['-v'])
# Extract path/file/extension
path_mt0, file_mt0, ext_mt0 = sct.extract_fname(fname_mt0)
path_out, file_out, ext_out = '', file_out, ext_mt0
# create temporary folder
path_tmp = sct.tmp_create()
# Copying input data to tmp folder and convert to nii
sct.printv('\nCopying input data to tmp folder and convert to nii...',
verbose)
from sct_convert import convert
convert(fname_mt0, os.path.join(path_tmp, "mt0.nii"), dtype=np.float32)
convert(fname_mt1, os.path.join(path_tmp, "mt1.nii"), dtype=np.float32)
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# compute MTR
sct.printv('\nCompute MTR...', verbose)
nii_mt1 = msct_image.Image('mt1.nii')
data_mt1 = nii_mt1.data
data_mt0 = msct_image.Image('mt0.nii').data
data_mtr = 100 * (data_mt0 - data_mt1) / data_mt0
# save MTR file
nii_mtr = nii_mt1
nii_mtr.data = data_mtr
nii_mtr.save("mtr.nii")
# sct.run(fsloutput+'fslmaths -dt double mt0.nii -sub mt1.nii -mul 100 -div mt0.nii -thr 0 -uthr 100 mtr.nii', verbose)
# come back
os.chdir(curdir)
# Generate output files
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(os.path.join(path_tmp, "mtr.nii"),
os.path.join(path_out, file_out + ext_out))
# Remove temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...')
sct.rmtree(path_tmp)
sct.display_viewer_syntax([fname_mt0, fname_mt1, file_out])
def main(args=None):
# initialization
start_time = time.time()
path_out = '.'
param = Param()
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
param.fname_data = arguments['-i']
param.fname_bvecs = arguments['-bvec']
if '-bval' in arguments:
param.fname_bvals = arguments['-bval']
if '-bvalmin' in arguments:
param.bval_min = arguments['-bvalmin']
if '-g' in arguments:
param.group_size = arguments['-g']
if '-m' in arguments:
param.fname_mask = arguments['-m']
if '-param' in arguments:
param.update(arguments['-param'])
if '-thr' in arguments:
param.otsu = arguments['-thr']
if '-x' in arguments:
param.interp = arguments['-x']
if '-ofolder' in arguments:
path_out = arguments['-ofolder']
if '-r' in arguments:
param.remove_temp_files = int(arguments['-r'])
param.verbose = int(arguments.get('-v'))
sct.init_sct(log_level=param.verbose, update=True) # Update log level
# Get full path
param.fname_data = os.path.abspath(param.fname_data)
param.fname_bvecs = os.path.abspath(param.fname_bvecs)
if param.fname_bvals != '':
param.fname_bvals = os.path.abspath(param.fname_bvals)
if param.fname_mask != '':
param.fname_mask = os.path.abspath(param.fname_mask)
# Extract path, file and extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
path_mask, file_mask, ext_mask = sct.extract_fname(param.fname_mask)
path_tmp = sct.tmp_create(basename="dmri_moco", verbose=param.verbose)
# names of files in temporary folder
mask_name = 'mask'
bvecs_fname = 'bvecs.txt'
# Copying input data to tmp folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', param.verbose)
convert(param.fname_data, os.path.join(path_tmp, "dmri.nii"))
sct.copy(param.fname_bvecs, os.path.join(path_tmp, bvecs_fname), verbose=param.verbose)
if param.fname_mask != '':
sct.copy(param.fname_mask, os.path.join(path_tmp, mask_name + ext_mask), verbose=param.verbose)
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# update field in param (because used later).
# TODO: make this cleaner...
if param.fname_mask != '':
param.fname_mask = mask_name + ext_mask
# run moco
fname_data_moco_tmp = dmri_moco(param)
# generate b0_moco_mean and dwi_moco_mean
args = ['-i', fname_data_moco_tmp, '-bvec', 'bvecs.txt', '-a', '1', '-v', '0']
if not param.fname_bvals == '':
# if bvals file is provided
args += ['-bval', param.fname_bvals]
fname_b0, fname_b0_mean, fname_dwi, fname_dwi_mean = sct_dmri_separate_b0_and_dwi.main(args=args)
# come back
os.chdir(curdir)
# Generate output files
fname_dmri_moco = os.path.join(path_out, file_data + param.suffix + ext_data)
fname_dmri_moco_b0_mean = sct.add_suffix(fname_dmri_moco, '_b0_mean')
fname_dmri_moco_dwi_mean = sct.add_suffix(fname_dmri_moco, '_dwi_mean')
sct.create_folder(path_out)
sct.printv('\nGenerate output files...', param.verbose)
sct.generate_output_file(fname_data_moco_tmp, fname_dmri_moco, param.verbose)
sct.generate_output_file(fname_b0_mean, fname_dmri_moco_b0_mean, param.verbose)
sct.generate_output_file(fname_dwi_mean, fname_dmri_moco_dwi_mean, param.verbose)
# Delete temporary files
if param.remove_temp_files == 1:
sct.printv('\nDelete temporary files...', param.verbose)
sct.rmtree(path_tmp, verbose=param.verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's', param.verbose)
sct.display_viewer_syntax([fname_dmri_moco, file_data], mode='ortho,ortho')
def main(args=None):
# initializations
param = Param()
# check user arguments
if args is None:
args = sys.argv[1:]
# get parser info
parser = get_parser()
arguments = parser.parse_args(args)
param.download = int(arguments.download)
param.path_data = arguments.path
functions_to_test = arguments.function
param.remove_tmp_file = int(arguments.remove_temps)
jobs = arguments.jobs
param.verbose = arguments.verbose
sct.init_sct(log_level=param.verbose, update=True) # Update log level
start_time = time.time()
# get absolute path and add slash at the end
param.path_data = os.path.abspath(param.path_data)
# check existence of testing data folder
if not os.path.isdir(param.path_data) or param.download:
downloaddata(param)
# display path to data
sct.printv('\nPath to testing data: ' + param.path_data, param.verbose)
# create temp folder that will have all results and go in it
path_tmp = os.path.abspath(arguments.execution_folder
or sct.tmp_create(verbose=param.verbose))
curdir = os.getcwd()
os.chdir(path_tmp)
functions_parallel = list()
functions_serial = list()
if functions_to_test:
for f in functions_to_test:
if f in get_functions_parallelizable():
functions_parallel.append(f)
elif f in get_functions_nonparallelizable():
functions_serial.append(f)
else:
sct.printv(
'Command-line usage error: Function "%s" is not part of the list of testing functions'
% f,
type='error')
jobs = min(jobs, len(functions_parallel))
else:
functions_parallel = get_functions_parallelizable()
functions_serial = get_functions_nonparallelizable()
if arguments.continue_from:
first_func = arguments.continue_from
if first_func in functions_parallel:
functions_serial = []
functions_parallel = functions_parallel[functions_parallel.
index(first_func):]
elif first_func in functions_serial:
functions_serial = functions_serial[functions_serial.
index(first_func):]
if arguments.check_filesystem and jobs != 1:
print("Check filesystem used -> jobs forced to 1")
jobs = 1
print("Will run through the following tests:")
if functions_serial:
print("- sequentially: {}".format(" ".join(functions_serial)))
if functions_parallel:
print("- in parallel with {} jobs: {}".format(
jobs, " ".join(functions_parallel)))
list_status = []
for name, functions in (
("serial", functions_serial),
("parallel", functions_parallel),
):
if not functions:
continue
if any([s for (f, s) in list_status]) and arguments.abort_on_failure:
break
try:
if functions == functions_parallel and jobs != 1:
pool = multiprocessing.Pool(processes=jobs)
results = list()
# loop across functions and run tests
for f in functions:
func_param = copy.deepcopy(param)
func_param.path_output = f
res = pool.apply_async(process_function_multiproc, (
f,
func_param,
))
results.append(res)
else:
pool = None
for idx_function, f in enumerate(functions):
print_line('Checking ' + f)
if functions == functions_serial or jobs == 1:
if arguments.check_filesystem:
if os.path.exists(os.path.join(path_tmp, f)):
shutil.rmtree(os.path.join(path_tmp, f))
sig_0 = fs_signature(path_tmp)
func_param = copy.deepcopy(param)
func_param.path_output = f
res = process_function(f, func_param)
if arguments.check_filesystem:
sig_1 = fs_signature(path_tmp)
fs_ok(sig_0, sig_1, exclude=(f, ))
else:
res = results[idx_function].get()
list_output, list_status_function = res
# manage status
if any(list_status_function):
if 1 in list_status_function:
print_fail()
status = (f, 1)
else:
print_warning()
status = (f, 99)
for output in list_output:
for line in output.splitlines():
print(" %s" % line)
else:
print_ok()
if param.verbose:
for output in list_output:
for line in output.splitlines():
print(" %s" % line)
status = (f, 0)
# append status function to global list of status
list_status.append(status)
if any([s for (f, s) in list_status
]) and arguments.abort_on_failure:
break
except KeyboardInterrupt:
raise
finally:
if pool:
pool.terminate()
pool.join()
print('status: ' + str([s for (f, s) in list_status]))
if any([s for (f, s) in list_status]):
print("Failures: {}".format(" ".join(
[f for (f, s) in list_status if s])))
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('Finished! Elapsed time: ' + str(int(np.round(elapsed_time))) +
's\n')
# come back
os.chdir(curdir)
# remove temp files
if param.remove_tmp_file and arguments.execution_folder is None:
sct.printv('\nRemove temporary files...', 0)
sct.rmtree(path_tmp)
e = 0
if any([s for (f, s) in list_status]):
e = 1
# print(e)
sys.exit(e)
def main(args=None):
if args is None:
args = sys.argv[1:]
# initialize parameters
param = Param()
# Initialization
fname_output = ''
path_out = ''
fname_src_seg = ''
fname_dest_seg = ''
fname_src_label = ''
fname_dest_label = ''
generate_warpinv = 1
start_time = time.time()
# get default registration parameters
# step1 = Paramreg(step='1', type='im', algo='syn', metric='MI', iter='5', shrink='1', smooth='0', gradStep='0.5')
step0 = Paramreg(
step='0',
type='im',
algo='syn',
metric='MI',
iter='0',
shrink='1',
smooth='0',
gradStep='0.5',
slicewise='0',
dof='Tx_Ty_Tz_Rx_Ry_Rz') # only used to put src into dest space
step1 = Paramreg(step='1', type='im')
paramreg = ParamregMultiStep([step0, step1])
parser = get_parser(paramreg=paramreg)
arguments = parser.parse(args)
# get arguments
fname_src = arguments['-i']
fname_dest = arguments['-d']
if '-iseg' in arguments:
fname_src_seg = arguments['-iseg']
if '-dseg' in arguments:
fname_dest_seg = arguments['-dseg']
if '-ilabel' in arguments:
fname_src_label = arguments['-ilabel']
if '-dlabel' in arguments:
fname_dest_label = arguments['-dlabel']
if '-o' in arguments:
fname_output = arguments['-o']
if '-ofolder' in arguments:
path_out = arguments['-ofolder']
if '-owarp' in arguments:
fname_output_warp = arguments['-owarp']
else:
fname_output_warp = ''
if '-initwarp' in arguments:
fname_initwarp = os.path.abspath(arguments['-initwarp'])
else:
fname_initwarp = ''
if '-initwarpinv' in arguments:
fname_initwarpinv = os.path.abspath(arguments['-initwarpinv'])
else:
fname_initwarpinv = ''
if '-m' in arguments:
fname_mask = arguments['-m']
else:
fname_mask = ''
padding = arguments['-z']
if "-param" in arguments:
paramreg_user = arguments['-param']
# update registration parameters
for paramStep in paramreg_user:
paramreg.addStep(paramStep)
path_qc = arguments.get("-qc", None)
qc_dataset = arguments.get("-qc-dataset", None)
qc_subject = arguments.get("-qc-subject", None)
identity = int(arguments['-identity'])
interp = arguments['-x']
remove_temp_files = int(arguments['-r'])
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
# sct.printv(arguments)
sct.printv('\nInput parameters:')
sct.printv(' Source .............. ' + fname_src)
sct.printv(' Destination ......... ' + fname_dest)
sct.printv(' Init transfo ........ ' + fname_initwarp)
sct.printv(' Mask ................ ' + fname_mask)
sct.printv(' Output name ......... ' + fname_output)
# sct.printv(' Algorithm ........... '+paramreg.algo)
# sct.printv(' Number of iterations '+paramreg.iter)
# sct.printv(' Metric .............. '+paramreg.metric)
sct.printv(' Remove temp files ... ' + str(remove_temp_files))
sct.printv(' Verbose ............. ' + str(verbose))
# update param
param.verbose = verbose
param.padding = padding
param.fname_mask = fname_mask
param.remove_temp_files = remove_temp_files
# Get if input is 3D
sct.printv('\nCheck if input data are 3D...', verbose)
sct.check_if_3d(fname_src)
sct.check_if_3d(fname_dest)
# Check if user selected type=seg, but did not input segmentation data
if 'paramreg_user' in locals():
if True in [
'type=seg' in paramreg_user[i]
for i in range(len(paramreg_user))
]:
if fname_src_seg == '' or fname_dest_seg == '':
sct.printv(
'\nERROR: if you select type=seg you must specify -iseg and -dseg flags.\n',
1, 'error')
# Extract path, file and extension
path_src, file_src, ext_src = sct.extract_fname(fname_src)
path_dest, file_dest, ext_dest = sct.extract_fname(fname_dest)
# check if source and destination images have the same name (related to issue #373)
# If so, change names to avoid conflict of result files and warns the user
suffix_src, suffix_dest = '_reg', '_reg'
if file_src == file_dest:
suffix_src, suffix_dest = '_src_reg', '_dest_reg'
# define output folder and file name
if fname_output == '':
path_out = '' if not path_out else path_out # output in user's current directory
file_out = file_src + suffix_src
file_out_inv = file_dest + suffix_dest
ext_out = ext_src
else:
path, file_out, ext_out = sct.extract_fname(fname_output)
path_out = path if not path_out else path_out
file_out_inv = file_out + '_inv'
# create temporary folder
path_tmp = sct.tmp_create()
sct.printv('\nCopying input data to tmp folder and convert to nii...',
verbose)
Image(fname_src).save(os.path.join(path_tmp, "src.nii"))
Image(fname_dest).save(os.path.join(path_tmp, "dest.nii"))
if fname_src_seg:
Image(fname_src_seg).save(os.path.join(path_tmp, "src_seg.nii"))
if fname_dest_seg:
Image(fname_dest_seg).save(os.path.join(path_tmp, "dest_seg.nii"))
if fname_src_label:
Image(fname_src_label).save(os.path.join(path_tmp, "src_label.nii"))
Image(fname_dest_label).save(os.path.join(path_tmp, "dest_label.nii"))
if fname_mask != '':
Image(fname_mask).save(os.path.join(path_tmp, "mask.nii.gz"))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# reorient destination to RPI
Image('dest.nii').change_orientation("RPI").save('dest_RPI.nii')
if fname_dest_seg:
Image('dest_seg.nii').change_orientation("RPI").save(
'dest_seg_RPI.nii')
if fname_dest_label:
Image('dest_label.nii').change_orientation("RPI").save(
'dest_label_RPI.nii')
if identity:
# overwrite paramreg and only do one identity transformation
step0 = Paramreg(step='0',
type='im',
algo='syn',
metric='MI',
iter='0',
shrink='1',
smooth='0',
gradStep='0.5')
paramreg = ParamregMultiStep([step0])
# Put source into destination space using header (no estimation -- purely based on header)
# TODO: Check if necessary to do that
# TODO: use that as step=0
# sct.printv('\nPut source into destination space using header...', verbose)
# sct.run('isct_antsRegistration -d 3 -t Translation[0] -m MI[dest_pad.nii,src.nii,1,16] -c 0 -f 1 -s 0 -o
# [regAffine,src_regAffine.nii] -n BSpline[3]', verbose)
# if segmentation, also do it for seg
# initialize list of warping fields
warp_forward = []
warp_forward_winv = []
warp_inverse = []
warp_inverse_winv = []
# initial warping is specified, update list of warping fields and skip step=0
if fname_initwarp:
sct.printv('\nSkip step=0 and replace with initial transformations: ',
param.verbose)
sct.printv(' ' + fname_initwarp, param.verbose)
# sct.copy(fname_initwarp, 'warp_forward_0.nii.gz')
warp_forward = [fname_initwarp]
start_step = 1
if fname_initwarpinv:
warp_inverse = [fname_initwarpinv]
else:
sct.printv(
'\nWARNING: No initial inverse warping field was specified, therefore the inverse warping field '
'will NOT be generated.', param.verbose, 'warning')
generate_warpinv = 0
else:
start_step = 0
# loop across registration steps
for i_step in range(start_step, len(paramreg.steps)):
sct.printv('\n--\nESTIMATE TRANSFORMATION FOR STEP #' + str(i_step),
param.verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = 'src.nii'
dest = 'dest_RPI.nii'
interp_step = 'spline'
elif paramreg.steps[str(i_step)].type == 'seg':
src = 'src_seg.nii'
dest = 'dest_seg_RPI.nii'
interp_step = 'nn'
elif paramreg.steps[str(i_step)].type == 'label':
src = 'src_label.nii'
dest = 'dest_label_RPI.nii'
interp_step = 'nn'
else:
# src = dest = interp_step = None
sct.printv('ERROR: Wrong image type.', 1, 'error')
# if step>0, apply warp_forward_concat to the src image to be used
if i_step > 0:
sct.printv('\nApply transformation from previous step',
param.verbose)
sct_apply_transfo.main(args=[
'-i', src, '-d', dest, '-w', warp_forward, '-o',
sct.add_suffix(src, '_reg'), '-x', interp_step
])
src = sct.add_suffix(src, '_reg')
# register src --> dest
warp_forward_out, warp_inverse_out = register(src, dest, paramreg,
param, str(i_step))
# deal with transformations with "-" as prefix. They should be inverted with calling sct_concat_transfo.
if warp_forward_out[0] == "-":
warp_forward_out = warp_forward_out[1:]
warp_forward_winv.append(warp_forward_out)
if warp_inverse_out[0] == "-":
warp_inverse_out = warp_inverse_out[1:]
warp_inverse_winv.append(warp_inverse_out)
# update list of forward/inverse transformations
warp_forward.append(warp_forward_out)
warp_inverse.insert(0, warp_inverse_out)
# Concatenate transformations
sct.printv('\nConcatenate transformations...', verbose)
sct_concat_transfo.main(args=[
'-w', warp_forward, '-winv', warp_forward_winv, '-d', 'dest.nii', '-o',
'warp_src2dest.nii.gz'
])
sct_concat_transfo.main(args=[
'-w', warp_inverse, '-winv', warp_inverse_winv, '-d', 'src.nii', '-o',
'warp_dest2src.nii.gz'
])
# Apply warping field to src data
sct.printv('\nApply transfo source --> dest...', verbose)
sct_apply_transfo.main(args=[
'-i', 'src.nii', '-d', 'dest.nii', '-w', 'warp_src2dest.nii.gz', '-o',
'src_reg.nii', '-x', interp
])
sct.printv('\nApply transfo dest --> source...', verbose)
sct_apply_transfo.main(args=[
'-i', 'dest.nii', '-d', 'src.nii', '-w', 'warp_dest2src.nii.gz', '-o',
'dest_reg.nii', '-x', interp
])
# come back
os.chdir(curdir)
# Generate output files
sct.printv('\nGenerate output files...', verbose)
# generate: src_reg
fname_src2dest = sct.generate_output_file(
os.path.join(path_tmp, "src_reg.nii"),
os.path.join(path_out, file_out + ext_out), verbose)
# generate: forward warping field
if fname_output_warp == '':
fname_output_warp = os.path.join(
path_out, 'warp_' + file_src + '2' + file_dest + '.nii.gz')
sct.generate_output_file(os.path.join(path_tmp, "warp_src2dest.nii.gz"),
fname_output_warp, verbose)
if generate_warpinv:
# generate: dest_reg
fname_dest2src = sct.generate_output_file(
os.path.join(path_tmp, "dest_reg.nii"),
os.path.join(path_out, file_out_inv + ext_dest), verbose)
# generate: inverse warping field
sct.generate_output_file(
os.path.join(path_tmp, "warp_dest2src.nii.gz"),
os.path.join(path_out,
'warp_' + file_dest + '2' + file_src + '.nii.gz'),
verbose)
# Delete temporary files
if remove_temp_files:
sct.printv('\nRemove temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv(
'\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's',
verbose)
if path_qc is not None:
if fname_dest_seg:
generate_qc(fname_src2dest,
fname_in2=fname_dest,
fname_seg=fname_dest_seg,
args=args,
path_qc=os.path.abspath(path_qc),
dataset=qc_dataset,
subject=qc_subject,
process='sct_register_multimodal')
else:
sct.printv(
'WARNING: Cannot generate QC because it requires destination segmentation.',
1, 'warning')
if generate_warpinv:
sct.display_viewer_syntax([fname_src, fname_dest2src], verbose=verbose)
sct.display_viewer_syntax([fname_dest, fname_src2dest], verbose=verbose)
def register(self):
# accentuate separation WM/GM
self.im_gm = thr_im(self.im_gm, 0.01, self.param.thr)
self.im_wm = thr_im(self.im_wm, 0.01, self.param.thr)
self.im_template_gm = thr_im(self.im_template_gm, 0.01, self.param.thr)
self.im_template_wm = thr_im(self.im_template_wm, 0.01, self.param.thr)
# create multilabel images:
# copy GM images to keep header information
im_automatic_ml = self.im_gm.copy()
im_template_ml = self.im_template_gm.copy()
# create multi-label segmentation with GM*200 + WM*100 (100 and 200 encoded in self.param.gap)
im_automatic_ml.data = self.param.gap[
1] * self.im_gm.data + self.param.gap[0] * self.im_wm.data
im_template_ml.data = self.param.gap[
1] * self.im_template_gm.data + self.param.gap[
0] * self.im_template_wm.data
# set new names
fname_automatic_ml = 'multilabel_automatic_seg.nii.gz'
fname_template_ml = 'multilabel_template_seg.nii.gz'
im_automatic_ml.setFileName(fname_automatic_ml)
im_template_ml.setFileName(fname_template_ml)
# Create temporary folder and put files in it
tmp_dir = sct.tmp_create()
path_gm, file_gm, ext_gm = sct.extract_fname(fname_gm)
path_warp_template2target, file_warp_template2target, ext_warp_template2target = sct.extract_fname(
self.fname_warp_template2target)
convert(fname_gm, tmp_dir + file_gm + ext_gm)
convert(fname_warp_template,
tmp_dir + file_warp_template2target + ext_warp_template2target,
squeeze_data=0)
if self.fname_warp_target2template is not None:
path_warp_target2template, file_warp_target2template, ext_warp_target2template = sct.extract_fname(
self.fname_warp_target2template)
convert(self.fname_warp_target2template,
tmp_dir + file_warp_target2template +
ext_warp_target2template,
squeeze_data=0)
os.chdir(tmp_dir)
# save images
im_automatic_ml.save()
im_template_ml.save()
# apply template2image warping field
if self.apply_warp_template == 1:
fname_template_ml_new = sct.add_suffix(fname_template_ml, '_r')
sct.run('sct_apply_transfo -i ' + fname_template_ml + ' -d ' +
fname_automatic_ml + ' -w ' + file_warp_template2target +
ext_warp_template2target + ' -o ' + fname_template_ml_new)
fname_template_ml = fname_template_ml_new
nx, ny, nz, nt, px, py, pz, pt = im_automatic_ml.dim
size_mask = int(22.5 / px)
fname_mask = 'square_mask.nii.gz'
sct.run('sct_create_mask -i ' + fname_automatic_ml +
' -p centerline,' + fname_automatic_ml + ' -f box -size ' +
str(size_mask) + ' -o ' + fname_mask)
fname_automatic_ml, xi, xf, yi, yf, zi, zf = crop_im(
fname_automatic_ml, fname_mask)
fname_template_ml, xi, xf, yi, yf, zi, zf = crop_im(
fname_template_ml, fname_mask)
# fname_automatic_ml_smooth = sct.add_suffix(fname_automatic_ml, '_smooth')
# sct.run('sct_maths -i '+fname_automatic_ml+' -smooth '+str(self.param.smooth)+','+str(self.param.smooth)+',0 -o '+fname_automatic_ml_smooth)
# fname_automatic_ml = fname_automatic_ml_smooth
path_automatic_ml, file_automatic_ml, ext_automatic_ml = sct.extract_fname(
fname_automatic_ml)
path_template_ml, file_template_ml, ext_template_ml = sct.extract_fname(
fname_template_ml)
# Register multilabel images together
cmd_reg = 'sct_register_multimodal -i ' + fname_template_ml + ' -d ' + fname_automatic_ml + ' -param ' + self.param.param_reg
if 'centermass' in self.param.param_reg:
fname_template_ml_seg = sct.add_suffix(fname_template_ml, '_bin')
sct.run('sct_maths -i ' + fname_template_ml + ' -bin 0 -o ' +
fname_template_ml_seg)
fname_automatic_ml_seg = sct.add_suffix(fname_automatic_ml, '_bin')
# sct.run('sct_maths -i '+fname_automatic_ml+' -thr 50 -o '+fname_automatic_ml_seg)
sct.run('sct_maths -i ' + fname_automatic_ml + ' -bin 50 -o ' +
fname_automatic_ml_seg)
cmd_reg += ' -iseg ' + fname_template_ml_seg + ' -dseg ' + fname_automatic_ml_seg
sct.run(cmd_reg)
fname_warp_multilabel_template2auto = 'warp_' + file_template_ml + '2' + file_automatic_ml + '.nii.gz'
fname_warp_multilabel_auto2template = 'warp_' + file_automatic_ml + '2' + file_template_ml + '.nii.gz'
self.fname_warp_template2gm = sct.extract_fname(
self.fname_warp_template2target
)[1] + '_reg_gm' + sct.extract_fname(
self.fname_warp_template2target)[2]
# fname_warp_multilabel_template2auto = pad_im(fname_warp_multilabel_template2auto, nx, ny, nz, xi, xf, yi, yf, zi, zf)
# fname_warp_multilabel_auto2template = pad_im(fname_warp_multilabel_auto2template, nx, ny, nz, xi, xf, yi, yf, zi, zf)
sct.run('sct_concat_transfo -w ' + file_warp_template2target +
ext_warp_template2target + ',' +
fname_warp_multilabel_template2auto + ' -d ' + file_gm +
ext_gm + ' -o ' + self.fname_warp_template2gm)
if self.fname_warp_target2template is not None:
path_script = os.path.dirname(__file__)
path_sct = os.path.dirname(path_script)
if self.template == 'MNI-Poly-AMU':
fname_dest = path_sct + '/data/MNI-Poly-AMU/template/MNI-Poly-AMU_T2.nii.gz'
elif self.template == 'PAM50':
fname_dest = path_sct + '/data/PAM50/template/PAM50_t2.nii.gz'
self.fname_warp_gm2template = sct.extract_fname(
self.fname_warp_target2template
)[1] + '_reg_gm' + sct.extract_fname(
self.fname_warp_target2template)[2]
sct.run('sct_concat_transfo -w ' +
fname_warp_multilabel_auto2template + ',' +
file_warp_target2template + ext_warp_target2template +
' -d ' + fname_dest + ' -o ' + self.fname_warp_gm2template)
os.chdir('..')
# sct.generate_output_file(tmp_dir+fname_warp_multilabel_template2auto, self.param.output_folder+'warp_template_multilabel2automatic_seg_multilabel.nii.gz')
# sct.generate_output_file(tmp_dir+fname_warp_multilabel_auto2template, self.param.output_folder+'warp_automatic_seg_multilabel2template_multilabel.nii.gz')
sct.generate_output_file(
tmp_dir + self.fname_warp_template2gm,
self.param.output_folder + self.fname_warp_template2gm)
if self.fname_warp_target2template is not None:
sct.generate_output_file(
tmp_dir + self.fname_warp_gm2template,
self.param.output_folder + self.fname_warp_gm2template)
if self.param.qc:
fname_grid_warped = visualize_warp(
tmp_dir + fname_warp_multilabel_template2auto,
rm_tmp=self.param.remove_tmp)
path_grid_warped, file_grid_warped, ext_grid_warped = sct.extract_fname(
fname_grid_warped)
sct.generate_output_file(
fname_grid_warped,
self.param.output_folder + file_grid_warped + ext_grid_warped)
if self.param.remove_tmp:
sct.run('rm -rf ' + tmp_dir, error_exit='warning')
def register_data(im_src,
im_dest,
param_reg,
path_copy_warp=None,
rm_tmp=True):
'''
Parameters
----------
im_src: class Image: source image
im_dest: class Image: destination image
param_reg: str: registration parameter
path_copy_warp: path: path to copy the warping fields
Returns: im_src_reg: class Image: source image registered on destination image
-------
'''
# im_src and im_dest are already preprocessed (in theory: im_dest = mean_image)
# binarize images to get seg
im_src_seg = binarize(im_src, thr_min=1, thr_max=1)
im_dest_seg = binarize(im_dest)
# create tmp dir and go in it
tmp_dir = sct.tmp_create()
curdir = os.getcwd()
os.chdir(tmp_dir)
# save image and seg
fname_src = 'src.nii.gz'
im_src.save(fname_src)
fname_src_seg = 'src_seg.nii.gz'
im_src_seg.save(fname_src_seg)
fname_dest = 'dest.nii.gz'
im_dest.save(fname_dest)
fname_dest_seg = 'dest_seg.nii.gz'
im_dest_seg.save(fname_dest_seg)
# do registration using param_reg
sct_register_multimodal.main(args=[
'-i', fname_src, '-d', fname_dest, '-iseg', fname_src_seg, '-dseg',
fname_dest_seg, '-param', param_reg
])
# get registration result
fname_src_reg = add_suffix(fname_src, '_reg')
im_src_reg = Image(fname_src_reg)
# get out of tmp dir
os.chdir(curdir)
# copy warping fields
if path_copy_warp is not None and os.path.isdir(
os.path.abspath(path_copy_warp)):
path_copy_warp = os.path.abspath(path_copy_warp)
file_src = extract_fname(fname_src)[1]
file_dest = extract_fname(fname_dest)[1]
fname_src2dest = 'warp_' + file_src + '2' + file_dest + '.nii.gz'
fname_dest2src = 'warp_' + file_dest + '2' + file_src + '.nii.gz'
sct.copy(os.path.join(tmp_dir, fname_src2dest), path_copy_warp)
sct.copy(os.path.join(tmp_dir, fname_dest2src), path_copy_warp)
if rm_tmp:
# remove tmp dir
sct.rmtree(tmp_dir)
# return res image
return im_src_reg, fname_src2dest, fname_dest2src
return parser
if __name__ == "__main__":
sct.init_sct()
parser = get_parser()
arguments = parser.parse_args(args=None if sys.argv[1:] else ['--help'])
fname_input1 = arguments.i
fname_input2 = arguments.d
verbose = arguments.v
sct.init_sct(log_level=verbose, update=True) # Update log level
tmp_dir = sct.tmp_create(verbose=verbose) # create tmp directory
tmp_dir = os.path.abspath(tmp_dir)
# copy input files to tmp directory
# for fname in [fname_input1, fname_input2]:
sct.copy(fname_input1, tmp_dir)
sct.copy(fname_input2, tmp_dir)
fname_input1 = ''.join(sct.extract_fname(fname_input1)[1:])
fname_input2 = ''.join(sct.extract_fname(fname_input2)[1:])
curdir = os.getcwd()
os.chdir(tmp_dir) # go to tmp directory
if '-bin' in arguments:
fname_input1_bin = sct.add_suffix(fname_input1, '_bin')
sct.run([
def pre_processing(fname_target,
fname_sc_seg,
fname_level=None,
fname_manual_gmseg=None,
new_res=0.3,
square_size_size_mm=22.5,
denoising=True,
verbose=1,
rm_tmp=True,
for_model=False):
printv('\nPre-process data...', verbose, 'normal')
tmp_dir = sct.tmp_create()
sct.copy(fname_target, tmp_dir)
fname_target = ''.join(extract_fname(fname_target)[1:])
sct.copy(fname_sc_seg, tmp_dir)
fname_sc_seg = ''.join(extract_fname(fname_sc_seg)[1:])
curdir = os.getcwd()
os.chdir(tmp_dir)
original_info = {
'orientation': None,
'im_sc_seg_rpi': None,
'interpolated_images': []
}
im_target = Image(fname_target).copy()
im_sc_seg = Image(fname_sc_seg).copy()
# get original orientation
printv(' Reorient...', verbose, 'normal')
original_info['orientation'] = im_target.orientation
# assert images are in the same orientation
assert im_target.orientation == im_sc_seg.orientation, "ERROR: the image to segment and it's SC segmentation are not in the same orientation"
im_target_rpi = im_target.copy().change_orientation(
'RPI', generate_path=True).save()
im_sc_seg_rpi = im_sc_seg.copy().change_orientation(
'RPI', generate_path=True).save()
original_info['im_sc_seg_rpi'] = im_sc_seg_rpi.copy(
) # target image in RPI will be used to post-process segmentations
# denoise using P. Coupe non local means algorithm (see [Manjon et al. JMRI 2010]) implemented in dipy
if denoising:
printv(' Denoise...', verbose, 'normal')
# crop image before denoising to fasten denoising
nx, ny, nz, nt, px, py, pz, pt = im_target_rpi.dim
size_x, size_y = (square_size_size_mm + 1) / px, (square_size_size_mm +
1) / py
size = int(np.ceil(max(size_x, size_y)))
# create mask
fname_mask = 'mask_pre_crop.nii.gz'
sct_create_mask.main([
'-i', im_target_rpi.absolutepath, '-p',
'centerline,' + im_sc_seg_rpi.absolutepath, '-f', 'box', '-size',
str(size), '-o', fname_mask
])
# crop image
cropper = ImageCropper(im_target_rpi)
cropper.get_bbox_from_mask(Image(fname_mask))
im_target_rpi_crop = cropper.crop()
# crop segmentation
cropper = ImageCropper(im_sc_seg_rpi)
cropper.get_bbox_from_mask(Image(fname_mask))
im_sc_seg_rpi_crop = cropper.crop()
# denoising
from spinalcordtoolbox.math import denoise_nlmeans
block_radius = 3
block_radius = int(
im_target_rpi_crop.data.shape[2] /
2) if im_target_rpi_crop.data.shape[2] < (block_radius *
2) else block_radius
patch_radius = block_radius - 1
data_denoised = denoise_nlmeans(im_target_rpi_crop.data,
block_radius=block_radius,
patch_radius=patch_radius)
im_target_rpi_crop.data = data_denoised
im_target_rpi = im_target_rpi_crop
im_sc_seg_rpi = im_sc_seg_rpi_crop
else:
fname_mask = None
# interpolate image to reference square image (resample and square crop centered on SC)
printv(' Interpolate data to the model space...', verbose, 'normal')
list_im_slices = interpolate_im_to_ref(im_target_rpi,
im_sc_seg_rpi,
new_res=new_res,
sq_size_size_mm=square_size_size_mm)
original_info[
'interpolated_images'] = list_im_slices # list of images (not Slice() objects)
printv(' Mask data using the spinal cord segmentation...', verbose,
'normal')
list_sc_seg_slices = interpolate_im_to_ref(
im_sc_seg_rpi,
im_sc_seg_rpi,
new_res=new_res,
sq_size_size_mm=square_size_size_mm,
interpolation_mode=1)
for i in range(len(list_im_slices)):
# list_im_slices[i].data[list_sc_seg_slices[i].data == 0] = 0
list_sc_seg_slices[i] = binarize(list_sc_seg_slices[i],
thr_min=0.5,
thr_max=1)
list_im_slices[
i].data = list_im_slices[i].data * list_sc_seg_slices[i].data
printv(' Split along rostro-caudal direction...', verbose, 'normal')
list_slices_target = [
Slice(slice_id=i, im=im_slice.data, gm_seg=[], wm_seg=[])
for i, im_slice in enumerate(list_im_slices)
]
# load vertebral levels
if fname_level is not None:
printv(' Load vertebral levels...', verbose, 'normal')
# copy level file to tmp dir
os.chdir(curdir)
sct.copy(fname_level, tmp_dir)
os.chdir(tmp_dir)
# change fname level to only file name (path = tmp dir now)
fname_level = ''.join(extract_fname(fname_level)[1:])
# load levels
list_slices_target = load_level(list_slices_target, fname_level)
os.chdir(curdir)
# load manual gmseg if there is one (model data)
if fname_manual_gmseg is not None:
printv('\n\tLoad manual GM segmentation(s) ...', verbose, 'normal')
list_slices_target = load_manual_gmseg(list_slices_target,
fname_manual_gmseg,
tmp_dir,
im_sc_seg_rpi,
new_res,
square_size_size_mm,
for_model=for_model,
fname_mask=fname_mask)
if rm_tmp:
# remove tmp folder
sct.rmtree(tmp_dir)
return list_slices_target, original_info
def main():
# Initialization
fname_mt0 = ''
fname_mt1 = ''
file_out = param.file_out
# register = param.register
# remove_tmp_files = param.remove_tmp_files
# verbose = param.verbose
# get path of the toolbox
# status, path_sct = commands.getstatusoutput('echo $SCT_DIR')
# Check input parameters
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
fname_mt0 = arguments['-mt0']
fname_mt1 = arguments['-mt1']
remove_tmp_files = int(arguments['-r'])
verbose = int(arguments['-v'])
# Extract path/file/extension
path_mt0, file_mt0, ext_mt0 = sct.extract_fname(fname_mt0)
path_out, file_out, ext_out = '', file_out, ext_mt0
# create temporary folder
path_tmp = sct.tmp_create()
# Copying input data to tmp folder and convert to nii
sct.printv('\nCopying input data to tmp folder and convert to nii...',
verbose)
from sct_convert import convert
convert(fname_mt0, path_tmp + 'mt0.nii', type='float32')
convert(fname_mt1, path_tmp + 'mt1.nii', type='float32')
# go to tmp folder
os.chdir(path_tmp)
# compute MTR
sct.printv('\nCompute MTR...', verbose)
from msct_image import Image
nii_mt1 = Image('mt1.nii')
data_mt1 = nii_mt1.data
data_mt0 = Image('mt0.nii').data
data_mtr = 100 * (data_mt0 - data_mt1) / data_mt0
# save MTR file
nii_mtr = nii_mt1
nii_mtr.data = data_mtr
nii_mtr.setFileName('mtr.nii')
nii_mtr.save()
# sct.run(fsloutput+'fslmaths -dt double mt0.nii -sub mt1.nii -mul 100 -div mt0.nii -thr 0 -uthr 100 mtr.nii', verbose)
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(path_tmp + 'mtr.nii',
path_out + file_out + ext_out)
# Remove temporary files
if remove_tmp_files == 1:
sct.printv('\nRemove temporary files...')
sct.run('rm -rf ' + path_tmp)
# to view results
sct.printv('\nDone! To view results, type:', verbose)
sct.printv(
'fslview ' + fname_mt0 + ' ' + fname_mt1 + ' ' + file_out + ' &\n',
verbose, 'info')
def straighten(self):
"""
Straighten spinal cord. Steps: (everything is done in physical space)
1. open input image and centreline image
2. extract bspline fitting of the centreline, and its derivatives
3. compute length of centerline
4. compute and generate straight space
5. compute transformations
for each voxel of one space: (done using matrices --> improves speed by a factor x300)
a. determine which plane of spinal cord centreline it is included
b. compute the position of the voxel in the plane (X and Y distance from centreline, along the plane)
c. find the correspondant centreline point in the other space
d. find the correspondance of the voxel in the corresponding plane
6. generate warping fields for each transformations
7. write warping fields and apply them
step 5.b: how to find the corresponding plane?
The centerline plane corresponding to a voxel correspond to the nearest point of the centerline.
However, we need to compute the distance between the voxel position and the plane to be sure it is part of the plane and not too distant.
If it is more far than a threshold, warping value should be 0.
step 5.d: how to make the correspondance between centerline point in both images?
Both centerline have the same lenght. Therefore, we can map centerline point via their position along the curve.
If we use the same number of points uniformely along the spinal cord (1000 for example), the correspondance is straight-forward.
:return:
"""
# Initialization
fname_anat = self.input_filename
fname_centerline = self.centerline_filename
fname_output = self.output_filename
remove_temp_files = self.remove_temp_files
verbose = self.verbose
interpolation_warp = self.interpolation_warp # TODO: remove this
# start timer
start_time = time.time()
# Extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
path_tmp = sct.tmp_create(basename="straighten_spinalcord",
verbose=verbose)
# Copying input data to tmp folder
sct.printv('\nCopy files to tmp folder...', verbose)
Image(fname_anat).save(os.path.join(path_tmp, "data.nii"))
Image(fname_centerline).save(
os.path.join(path_tmp, "centerline.nii.gz"))
if self.use_straight_reference:
Image(self.centerline_reference_filename).save(
os.path.join(path_tmp, "centerline_ref.nii.gz"))
if self.discs_input_filename != '':
Image(self.discs_input_filename).save(
os.path.join(path_tmp, "labels_input.nii.gz"))
if self.discs_ref_filename != '':
Image(self.discs_ref_filename).save(
os.path.join(path_tmp, "labels_ref.nii.gz"))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Change orientation of the input centerline into RPI
image_centerline = Image("centerline.nii.gz").change_orientation(
"RPI").save("centerline_rpi.nii.gz", mutable=True)
# Get dimension
nx, ny, nz, nt, px, py, pz, pt = image_centerline.dim
if self.speed_factor != 1.0:
intermediate_resampling = True
px_r, py_r, pz_r = px * self.speed_factor, py * self.speed_factor, pz * self.speed_factor
else:
intermediate_resampling = False
if intermediate_resampling:
sct.mv('centerline_rpi.nii.gz', 'centerline_rpi_native.nii.gz')
pz_native = pz
# TODO: remove system call
sct.run([
'sct_resample', '-i', 'centerline_rpi_native.nii.gz', '-mm',
str(px_r) + 'x' + str(py_r) + 'x' + str(pz_r), '-o',
'centerline_rpi.nii.gz'
])
image_centerline = Image('centerline_rpi.nii.gz')
nx, ny, nz, nt, px, py, pz, pt = image_centerline.dim
if np.min(image_centerline.data) < 0 or np.max(
image_centerline.data) > 1:
image_centerline.data[image_centerline.data < 0] = 0
image_centerline.data[image_centerline.data > 1] = 1
image_centerline.save()
# 2. extract bspline fitting of the centerline, and its derivatives
img_ctl = Image('centerline_rpi.nii.gz')
centerline = _get_centerline(img_ctl, self.param_centerline, verbose)
number_of_points = centerline.number_of_points
# ==========================================================================================
logger.info('Create the straight space and the safe zone')
# 3. compute length of centerline
# compute the length of the spinal cord based on fitted centerline and size of centerline in z direction
# Computation of the safe zone.
# The safe zone is defined as the length of the spinal cord for which an axial segmentation will be complete
# The safe length (to remove) is computed using the safe radius (given as parameter) and the angle of the
# last centerline point with the inferior-superior direction. Formula: Ls = Rs * sin(angle)
# Calculate Ls for both edges and remove appropriate number of centerline points
radius_safe = 0.0 # mm
# inferior edge
u = centerline.derivatives[0]
v = np.array([0, 0, -1])
angle_inferior = np.arctan2(np.linalg.norm(np.cross(u, v)),
np.dot(u, v))
length_safe_inferior = radius_safe * np.sin(angle_inferior)
# superior edge
u = centerline.derivatives[-1]
v = np.array([0, 0, 1])
angle_superior = np.arctan2(np.linalg.norm(np.cross(u, v)),
np.dot(u, v))
length_safe_superior = radius_safe * np.sin(angle_superior)
# remove points
inferior_bound = bisect.bisect(centerline.progressive_length,
length_safe_inferior) - 1
superior_bound = centerline.number_of_points - bisect.bisect(
centerline.progressive_length_inverse, length_safe_superior)
z_centerline = centerline.points[:, 2]
length_centerline = centerline.length
size_z_centerline = z_centerline[-1] - z_centerline[0]
# compute the size factor between initial centerline and straight bended centerline
factor_curved_straight = length_centerline / size_z_centerline
middle_slice = (z_centerline[0] + z_centerline[-1]) / 2.0
bound_curved = [
z_centerline[inferior_bound], z_centerline[superior_bound]
]
bound_straight = [(z_centerline[inferior_bound] - middle_slice) *
factor_curved_straight + middle_slice,
(z_centerline[superior_bound] - middle_slice) *
factor_curved_straight + middle_slice]
logger.info('Length of spinal cord: {}'.format(length_centerline))
logger.info(
'Size of spinal cord in z direction: {}'.format(size_z_centerline))
logger.info('Ratio length/size: {}'.format(factor_curved_straight))
logger.info(
'Safe zone boundaries (curved space): {}'.format(bound_curved))
logger.info(
'Safe zone boundaries (straight space): {}'.format(bound_straight))
# 4. compute and generate straight space
# points along curved centerline are already regularly spaced.
# calculate position of points along straight centerline
# Create straight NIFTI volumes.
# ==========================================================================================
# TODO: maybe this if case is not needed?
if self.use_straight_reference:
image_centerline_pad = Image('centerline_rpi.nii.gz')
nx, ny, nz, nt, px, py, pz, pt = image_centerline_pad.dim
fname_ref = 'centerline_ref_rpi.nii.gz'
image_centerline_straight = Image('centerline_ref.nii.gz') \
.change_orientation("RPI") \
.save(fname_ref, mutable=True)
centerline_straight = _get_centerline(image_centerline_straight,
algo_fitting, self.degree,
verbose)
nx_s, ny_s, nz_s, nt_s, px_s, py_s, pz_s, pt_s = image_centerline_straight.dim
# Prepare warping fields headers
hdr_warp = image_centerline_pad.hdr.copy()
hdr_warp.set_data_dtype('float32')
hdr_warp_s = image_centerline_straight.hdr.copy()
hdr_warp_s.set_data_dtype('float32')
if self.discs_input_filename != "" and self.discs_ref_filename != "":
discs_input_image = Image('labels_input.nii.gz')
coord = discs_input_image.getNonZeroCoordinates(
sorting='z', reverse_coord=True)
coord_physical = []
for c in coord:
c_p = discs_input_image.transfo_pix2phys([[c.x, c.y, c.z]
]).tolist()[0]
c_p.append(c.value)
coord_physical.append(c_p)
centerline.compute_vertebral_distribution(coord_physical)
centerline.save_centerline(
image=discs_input_image,
fname_output='discs_input_image.nii.gz')
discs_ref_image = Image('labels_ref.nii.gz')
coord = discs_ref_image.getNonZeroCoordinates(
sorting='z', reverse_coord=True)
coord_physical = []
for c in coord:
c_p = discs_ref_image.transfo_pix2phys([[c.x, c.y,
c.z]]).tolist()[0]
c_p.append(c.value)
coord_physical.append(c_p)
centerline_straight.compute_vertebral_distribution(
coord_physical)
centerline_straight.save_centerline(
image=discs_ref_image,
fname_output='discs_ref_image.nii.gz')
else:
logger.info(
'Pad input volume to account for spinal cord length...')
start_point, end_point = bound_straight[0], bound_straight[1]
offset_z = 0
# if the destination image is resampled, we still create the straight reference space with the native
# resolution.
# TODO: Maybe this if case is not needed?
if intermediate_resampling:
padding_z = int(
np.ceil(1.5 *
((length_centerline - size_z_centerline) / 2.0) /
pz_native))
sct.run([
'sct_image', '-i', 'centerline_rpi_native.nii.gz', '-o',
'tmp.centerline_pad_native.nii.gz', '-pad',
'0,0,' + str(padding_z)
])
image_centerline_pad = Image('centerline_rpi_native.nii.gz')
nx, ny, nz, nt, px, py, pz, pt = image_centerline_pad.dim
start_point_coord_native = image_centerline_pad.transfo_phys2pix(
[[0, 0, start_point]])[0]
end_point_coord_native = image_centerline_pad.transfo_phys2pix(
[[0, 0, end_point]])[0]
straight_size_x = int(self.xy_size / px)
straight_size_y = int(self.xy_size / py)
warp_space_x = [
int(np.round(nx / 2)) - straight_size_x,
int(np.round(nx / 2)) + straight_size_x
]
warp_space_y = [
int(np.round(ny / 2)) - straight_size_y,
int(np.round(ny / 2)) + straight_size_y
]
if warp_space_x[0] < 0:
warp_space_x[1] += warp_space_x[0] - 2
warp_space_x[0] = 0
if warp_space_y[0] < 0:
warp_space_y[1] += warp_space_y[0] - 2
warp_space_y[0] = 0
spec = dict((
(0, warp_space_x),
(1, warp_space_y),
(2, (0, end_point_coord_native[2] -
start_point_coord_native[2])),
))
msct_image.spatial_crop(
Image("tmp.centerline_pad_native.nii.gz"),
spec).save("tmp.centerline_pad_crop_native.nii.gz")
fname_ref = 'tmp.centerline_pad_crop_native.nii.gz'
offset_z = 4
else:
fname_ref = 'tmp.centerline_pad_crop.nii.gz'
nx, ny, nz, nt, px, py, pz, pt = image_centerline.dim
padding_z = int(
np.ceil(1.5 * ((length_centerline - size_z_centerline) / 2.0) /
pz)) + offset_z
image_centerline_pad = pad_image(image_centerline,
pad_z_i=padding_z,
pad_z_f=padding_z)
nx, ny, nz = image_centerline_pad.data.shape
hdr_warp = image_centerline_pad.hdr.copy()
hdr_warp.set_data_dtype('float32')
start_point_coord = image_centerline_pad.transfo_phys2pix(
[[0, 0, start_point]])[0]
end_point_coord = image_centerline_pad.transfo_phys2pix(
[[0, 0, end_point]])[0]
straight_size_x = int(self.xy_size / px)
straight_size_y = int(self.xy_size / py)
warp_space_x = [
int(np.round(nx / 2)) - straight_size_x,
int(np.round(nx / 2)) + straight_size_x
]
warp_space_y = [
int(np.round(ny / 2)) - straight_size_y,
int(np.round(ny / 2)) + straight_size_y
]
if warp_space_x[0] < 0:
warp_space_x[1] += warp_space_x[0] - 2
warp_space_x[0] = 0
if warp_space_x[1] >= nx:
warp_space_x[1] = nx - 1
if warp_space_y[0] < 0:
warp_space_y[1] += warp_space_y[0] - 2
warp_space_y[0] = 0
if warp_space_y[1] >= ny:
warp_space_y[1] = ny - 1
spec = dict((
(0, warp_space_x),
(1, warp_space_y),
(2, (0, end_point_coord[2] - start_point_coord[2] + offset_z)),
))
image_centerline_straight = msct_image.spatial_crop(
image_centerline_pad, spec)
nx_s, ny_s, nz_s, nt_s, px_s, py_s, pz_s, pt_s = image_centerline_straight.dim
hdr_warp_s = image_centerline_straight.hdr.copy()
hdr_warp_s.set_data_dtype('float32')
if self.template_orientation == 1:
raise NotImplementedError()
start_point_coord = image_centerline_pad.transfo_phys2pix(
[[0, 0, start_point]])[0]
end_point_coord = image_centerline_pad.transfo_phys2pix(
[[0, 0, end_point]])[0]
number_of_voxel = nx * ny * nz
logger.debug('Number of voxels: {}'.format(number_of_voxel))
time_centerlines = time.time()
coord_straight = np.empty((number_of_points, 3))
coord_straight[..., 0] = int(np.round(nx_s / 2))
coord_straight[..., 1] = int(np.round(ny_s / 2))
coord_straight[..., 2] = np.linspace(
0, end_point_coord[2] - start_point_coord[2], number_of_points)
coord_phys_straight = image_centerline_straight.transfo_pix2phys(
coord_straight)
derivs_straight = np.empty((number_of_points, 3))
derivs_straight[..., 0] = derivs_straight[..., 1] = 0
derivs_straight[..., 2] = 1
dx_straight, dy_straight, dz_straight = derivs_straight.T
centerline_straight = Centerline(coord_phys_straight[:, 0],
coord_phys_straight[:, 1],
coord_phys_straight[:, 2],
dx_straight, dy_straight,
dz_straight)
time_centerlines = time.time() - time_centerlines
logger.info('Time to generate centerline: {} ms'.format(
np.round(time_centerlines * 1000.0)))
if verbose == 2:
# TODO: use OO
import matplotlib.pyplot as plt
from datetime import datetime
curved_points = centerline.progressive_length
straight_points = centerline_straight.progressive_length
range_points = np.linspace(0, 1, number_of_points)
dist_curved = np.zeros(number_of_points)
dist_straight = np.zeros(number_of_points)
for i in range(1, number_of_points):
dist_curved[i] = dist_curved[
i - 1] + curved_points[i - 1] / centerline.length
dist_straight[i] = dist_straight[i - 1] + straight_points[
i - 1] / centerline_straight.length
plt.plot(range_points, dist_curved)
plt.plot(range_points, dist_straight)
plt.grid(True)
plt.savefig('fig_straighten_' +
datetime.now().strftime("%y%m%d%H%M%S%f") + '.png')
plt.close()
# alignment_mode = 'length'
alignment_mode = 'levels'
lookup_curved2straight = list(range(centerline.number_of_points))
if self.discs_input_filename != "":
# create look-up table curved to straight
for index in range(centerline.number_of_points):
disc_label = centerline.l_points[index]
if alignment_mode == 'length':
relative_position = centerline.dist_points[index]
else:
relative_position = centerline.dist_points_rel[index]
idx_closest = centerline_straight.get_closest_to_absolute_position(
disc_label,
relative_position,
backup_index=index,
backup_centerline=centerline_straight,
mode=alignment_mode)
if idx_closest is not None:
lookup_curved2straight[index] = idx_closest
else:
lookup_curved2straight[index] = 0
for p in range(0, len(lookup_curved2straight) // 2):
if lookup_curved2straight[p] == lookup_curved2straight[p + 1]:
lookup_curved2straight[p] = 0
else:
break
for p in range(
len(lookup_curved2straight) - 1,
len(lookup_curved2straight) // 2, -1):
if lookup_curved2straight[p] == lookup_curved2straight[p - 1]:
lookup_curved2straight[p] = 0
else:
break
lookup_curved2straight = np.array(lookup_curved2straight)
lookup_straight2curved = list(
range(centerline_straight.number_of_points))
if self.discs_input_filename != "":
for index in range(centerline_straight.number_of_points):
disc_label = centerline_straight.l_points[index]
if alignment_mode == 'length':
relative_position = centerline_straight.dist_points[index]
else:
relative_position = centerline_straight.dist_points_rel[
index]
idx_closest = centerline.get_closest_to_absolute_position(
disc_label,
relative_position,
backup_index=index,
backup_centerline=centerline_straight,
mode=alignment_mode)
if idx_closest is not None:
lookup_straight2curved[index] = idx_closest
for p in range(0, len(lookup_straight2curved) // 2):
if lookup_straight2curved[p] == lookup_straight2curved[p + 1]:
lookup_straight2curved[p] = 0
else:
break
for p in range(
len(lookup_straight2curved) - 1,
len(lookup_straight2curved) // 2, -1):
if lookup_straight2curved[p] == lookup_straight2curved[p - 1]:
lookup_straight2curved[p] = 0
else:
break
lookup_straight2curved = np.array(lookup_straight2curved)
# Create volumes containing curved and straight warping fields
data_warp_curved2straight = np.zeros((nx_s, ny_s, nz_s, 1, 3))
data_warp_straight2curved = np.zeros((nx, ny, nz, 1, 3))
# 5. compute transformations
# Curved and straight images and the same dimensions, so we compute both warping fields at the same time.
# b. determine which plane of spinal cord centreline it is included
# sct.printv(nx * ny * nz, nx_s * ny_s * nz_s)
if self.curved2straight:
for u in tqdm(range(nz_s)):
x_s, y_s, z_s = np.mgrid[0:nx_s, 0:ny_s, u:u + 1]
indexes_straight = np.array(
list(zip(x_s.ravel(), y_s.ravel(), z_s.ravel())))
physical_coordinates_straight = image_centerline_straight.transfo_pix2phys(
indexes_straight)
nearest_indexes_straight = centerline_straight.find_nearest_indexes(
physical_coordinates_straight)
distances_straight = centerline_straight.get_distances_from_planes(
physical_coordinates_straight, nearest_indexes_straight)
lookup = lookup_straight2curved[nearest_indexes_straight]
indexes_out_distance_straight = np.logical_or(
np.logical_or(
distances_straight > self.threshold_distance,
distances_straight < -self.threshold_distance),
lookup == 0)
projected_points_straight = centerline_straight.get_projected_coordinates_on_planes(
physical_coordinates_straight, nearest_indexes_straight)
coord_in_planes_straight = centerline_straight.get_in_plans_coordinates(
projected_points_straight, nearest_indexes_straight)
coord_straight2curved = centerline.get_inverse_plans_coordinates(
coord_in_planes_straight, lookup)
displacements_straight = coord_straight2curved - physical_coordinates_straight
# Invert Z coordinate as ITK & ANTs physical coordinate system is LPS- (RAI+)
# while ours is LPI-
# Refs: https://sourceforge.net/p/advants/discussion/840261/thread/2a1e9307/#fb5a
# https://www.slicer.org/wiki/Coordinate_systems
displacements_straight[:, 2] = -displacements_straight[:, 2]
displacements_straight[indexes_out_distance_straight] = [
100000.0, 100000.0, 100000.0
]
data_warp_curved2straight[indexes_straight[:, 0], indexes_straight[:, 1], indexes_straight[:, 2], 0, :]\
= -displacements_straight
if self.straight2curved:
for u in tqdm(range(nz)):
x, y, z = np.mgrid[0:nx, 0:ny, u:u + 1]
indexes = np.array(list(zip(x.ravel(), y.ravel(), z.ravel())))
physical_coordinates = image_centerline_pad.transfo_pix2phys(
indexes)
nearest_indexes_curved = centerline.find_nearest_indexes(
physical_coordinates)
distances_curved = centerline.get_distances_from_planes(
physical_coordinates, nearest_indexes_curved)
lookup = lookup_curved2straight[nearest_indexes_curved]
indexes_out_distance_curved = np.logical_or(
np.logical_or(distances_curved > self.threshold_distance,
distances_curved < -self.threshold_distance),
lookup == 0)
projected_points_curved = centerline.get_projected_coordinates_on_planes(
physical_coordinates, nearest_indexes_curved)
coord_in_planes_curved = centerline.get_in_plans_coordinates(
projected_points_curved, nearest_indexes_curved)
coord_curved2straight = centerline_straight.points[lookup]
coord_curved2straight[:, 0:2] += coord_in_planes_curved[:, 0:2]
coord_curved2straight[:, 2] += distances_curved
displacements_curved = coord_curved2straight - physical_coordinates
displacements_curved[:, 2] = -displacements_curved[:, 2]
displacements_curved[indexes_out_distance_curved] = [
100000.0, 100000.0, 100000.0
]
data_warp_straight2curved[indexes[:, 0], indexes[:, 1],
indexes[:, 2],
0, :] = -displacements_curved
# Creation of the safe zone based on pre-calculated safe boundaries
coord_bound_curved_inf, coord_bound_curved_sup = image_centerline_pad.transfo_phys2pix(
[[0, 0, bound_curved[0]]]), image_centerline_pad.transfo_phys2pix(
[[0, 0, bound_curved[1]]])
coord_bound_straight_inf, coord_bound_straight_sup = image_centerline_straight.transfo_phys2pix(
[[0, 0,
bound_straight[0]]]), image_centerline_straight.transfo_phys2pix(
[[0, 0, bound_straight[1]]])
if radius_safe > 0:
data_warp_curved2straight[:, :, 0:coord_bound_straight_inf[0][2],
0, :] = 100000.0
data_warp_curved2straight[:, :, coord_bound_straight_sup[0][2]:,
0, :] = 100000.0
data_warp_straight2curved[:, :, 0:coord_bound_curved_inf[0][2],
0, :] = 100000.0
data_warp_straight2curved[:, :, coord_bound_curved_sup[0][2]:,
0, :] = 100000.0
# Generate warp files as a warping fields
hdr_warp_s.set_intent('vector', (), '')
hdr_warp_s.set_data_dtype('float32')
hdr_warp.set_intent('vector', (), '')
hdr_warp.set_data_dtype('float32')
if self.curved2straight:
img = Nifti1Image(data_warp_curved2straight, None, hdr_warp_s)
save(img, 'tmp.curve2straight.nii.gz')
logger.info('Warping field generated: tmp.curve2straight.nii.gz')
if self.straight2curved:
img = Nifti1Image(data_warp_straight2curved, None, hdr_warp)
save(img, 'tmp.straight2curve.nii.gz')
logger.info('Warping field generated: tmp.straight2curve.nii.gz')
image_centerline_straight.save(fname_ref)
if self.curved2straight:
logger.info('Apply transformation to input image...')
sct.run([
'isct_antsApplyTransforms', '-d', '3', '-r', fname_ref, '-i',
'data.nii', '-o', 'tmp.anat_rigid_warp.nii.gz', '-t',
'tmp.curve2straight.nii.gz', '-n', 'BSpline[3]'
],
is_sct_binary=True,
verbose=verbose)
if self.accuracy_results:
time_accuracy_results = time.time()
# compute the error between the straightened centerline/segmentation and the central vertical line.
# Ideally, the error should be zero.
# Apply deformation to input image
logger.info('Apply transformation to centerline image...')
sct.run([
'isct_antsApplyTransforms', '-d', '3', '-r', fname_ref, '-i',
'centerline.nii.gz', '-o', 'tmp.centerline_straight.nii.gz',
'-t', 'tmp.curve2straight.nii.gz', '-n', 'NearestNeighbor'
],
is_sct_binary=True,
verbose=verbose)
file_centerline_straight = Image('tmp.centerline_straight.nii.gz',
verbose=verbose)
nx, ny, nz, nt, px, py, pz, pt = file_centerline_straight.dim
coordinates_centerline = file_centerline_straight.getNonZeroCoordinates(
sorting='z')
mean_coord = []
for z in range(coordinates_centerline[0].z,
coordinates_centerline[-1].z):
temp_mean = [
coord.value for coord in coordinates_centerline
if coord.z == z
]
if temp_mean:
mean_value = np.mean(temp_mean)
mean_coord.append(
np.mean([[
coord.x * coord.value / mean_value,
coord.y * coord.value / mean_value
] for coord in coordinates_centerline if coord.z == z],
axis=0))
# compute error between the straightened centerline and the straight line.
x0 = file_centerline_straight.data.shape[0] / 2.0
y0 = file_centerline_straight.data.shape[1] / 2.0
count_mean = 0
if number_of_points >= 10:
mean_c = mean_coord[
2:
-2] # we don't include the four extrema because there are usually messy.
else:
mean_c = mean_coord
for coord_z in mean_c:
if not np.isnan(np.sum(coord_z)):
dist = ((x0 - coord_z[0]) * px)**2 + (
(y0 - coord_z[1]) * py)**2
self.mse_straightening += dist
dist = np.sqrt(dist)
if dist > self.max_distance_straightening:
self.max_distance_straightening = dist
count_mean += 1
self.mse_straightening = np.sqrt(self.mse_straightening /
float(count_mean))
self.elapsed_time_accuracy = time.time() - time_accuracy_results
os.chdir(curdir)
# Generate output file (in current folder)
# TODO: do not uncompress the warping field, it is too time consuming!
logger.info('Generate output files...')
if self.curved2straight:
sct.generate_output_file(
os.path.join(path_tmp, "tmp.curve2straight.nii.gz"),
os.path.join(self.path_output, "warp_curve2straight.nii.gz"),
verbose)
if self.straight2curved:
sct.generate_output_file(
os.path.join(path_tmp, "tmp.straight2curve.nii.gz"),
os.path.join(self.path_output, "warp_straight2curve.nii.gz"),
verbose)
# create ref_straight.nii.gz file that can be used by other SCT functions that need a straight reference space
if self.curved2straight:
sct.copy(os.path.join(path_tmp, "tmp.anat_rigid_warp.nii.gz"),
os.path.join(self.path_output, "straight_ref.nii.gz"))
# move straightened input file
if fname_output == '':
fname_straight = sct.generate_output_file(
os.path.join(path_tmp, "tmp.anat_rigid_warp.nii.gz"),
os.path.join(self.path_output,
file_anat + "_straight" + ext_anat), verbose)
else:
fname_straight = sct.generate_output_file(
os.path.join(path_tmp, "tmp.anat_rigid_warp.nii.gz"),
os.path.join(self.path_output, fname_output),
verbose) # straightened anatomic
# Remove temporary files
if remove_temp_files:
logger.info('Remove temporary files...')
sct.rmtree(path_tmp)
if self.accuracy_results:
logger.info('Maximum x-y error: {} mm'.format(
self.max_distance_straightening))
logger.info('Accuracy of straightening (MSE): {} mm'.format(
self.mse_straightening))
# display elapsed time
self.elapsed_time = int(np.round(time.time() - start_time))
return fname_straight
def validation(self, fname_manual_gmseg, fname_sc_seg):
path_manual_gmseg, file_manual_gmseg, ext_manual_gmseg = sct.extract_fname(
fname_manual_gmseg)
path_sc_seg, file_sc_seg, ext_sc_seg = sct.extract_fname(fname_sc_seg)
# Create tmp folder and copy files in it
tmp_dir = sct.tmp_create()
sct.run('cp ' + fname_manual_gmseg + ' ' + tmp_dir +
file_manual_gmseg + ext_manual_gmseg)
sct.run('cp ' + fname_sc_seg + ' ' + tmp_dir + file_sc_seg +
ext_sc_seg)
sct.run('cp ' + self.param.output_folder +
self.fname_warp_template2gm + ' ' + tmp_dir +
self.fname_warp_template2gm)
os.chdir(tmp_dir)
sct.run('sct_warp_template -d ' + fname_manual_gmseg + ' -w ' +
self.fname_warp_template2gm + ' -qc 0 -a 0')
if 'MNI-Poly-AMU_GM.nii.gz' in os.listdir('label/template/'):
im_new_template_gm = Image('label/template/MNI-Poly-AMU_GM.nii.gz')
im_new_template_wm = Image('label/template/MNI-Poly-AMU_WM.nii.gz')
else:
im_new_template_gm = Image('label/template/PAM50_gm.nii.gz')
im_new_template_wm = Image('label/template/PAM50_wm.nii.gz')
im_new_template_gm = thr_im(im_new_template_gm, self.param.thr,
self.param.thr)
im_new_template_wm = thr_im(im_new_template_wm, self.param.thr,
self.param.thr)
self.im_template_gm = thr_im(self.im_template_gm, self.param.thr,
self.param.thr)
self.im_template_wm = thr_im(self.im_template_wm, self.param.thr,
self.param.thr)
fname_new_template_gm = 'new_template_gm.nii.gz'
im_new_template_gm.setFileName(fname_new_template_gm)
im_new_template_gm.save()
fname_new_template_wm = 'new_template_wm.nii.gz'
im_new_template_wm.setFileName(fname_new_template_wm)
im_new_template_wm.save()
fname_old_template_wm = 'old_template_wm.nii.gz'
self.im_template_wm.setFileName(fname_old_template_wm)
self.im_template_wm.save()
fname_old_template_gm = 'old_template_gm.nii.gz'
self.im_template_gm.setFileName(fname_old_template_gm)
self.im_template_gm.save()
fname_manual_wmseg = 'target_manual_wmseg.nii.gz'
sct.run('sct_maths -i ' + file_sc_seg + ext_sc_seg + ' -sub ' +
file_manual_gmseg + ext_manual_gmseg + ' -o ' +
fname_manual_wmseg)
# Compute Hausdorff distance
status, output_old_hd = sct.run('sct_compute_hausdorff_distance -i ' +
fname_old_template_gm + ' -r ' +
file_manual_gmseg + ext_manual_gmseg +
' -t 1 -v 1')
status, output_new_hd = sct.run('sct_compute_hausdorff_distance -i ' +
fname_new_template_gm + ' -r ' +
file_manual_gmseg + ext_manual_gmseg +
' -t 1 -v 1')
hd_name = 'hd_md_multilabel_reg.txt'
hd_fic = open(hd_name, 'w')
hd_fic.write(
'The "diff" columns are comparisons between regular template registration and corrected template registration according to SC internal structure\n'
'Diff = metric_regular_reg - metric_corrected_reg\n')
hd_fic.write('#Slice, HD, HD diff, MD, MD diff\n')
no_ref_slices = []
init_hd = "Hausdorff's distance - First relative Hausdorff's distance median - Second relative Hausdorff's distance median(all in mm)\n"
old_gm_hd = output_old_hd[output_old_hd.find(init_hd) +
len(init_hd):].split('\n')
new_gm_hd = output_new_hd[output_new_hd.find(init_hd) +
len(init_hd):].split('\n')
for i in range(len(old_gm_hd) - 3): # last two lines are informations
i_old, val_old = old_gm_hd[i].split(':')
i_new, val_new = new_gm_hd[i].split(':')
i_old = int(i_old[-2:])
i_new = int(i_new[-2:])
assert i == i_old == i_new, 'ERROR: when comparing Hausdorff distances, slice numbers differs.'
hd_old, med1_old, med2_old = val_old.split('-')
hd_new, med1_new, med2_new = val_new.split('-')
if float(hd_old) == 0.0:
no_ref_slices.append(i)
hd_fic.write(str(i) + ', NO MANUAL SEGMENTATION\n')
else:
md_new = max(float(med1_new), float(med2_new))
md_old = max(float(med1_old), float(med2_old))
hd_fic.write(
str(i) + ', ' + hd_new + ', ' +
str(float(hd_old) - float(hd_new)) + ', ' + str(md_new) +
', ' + str(md_old - md_new) + '\n')
hd_fic.close()
# Compute Dice coefficient
# --- DC old template
try:
status_old_gm, output_old_gm = sct.run(
'sct_dice_coefficient -i ' + file_manual_gmseg +
ext_manual_gmseg + ' -d ' + fname_old_template_gm +
' -2d-slices 2',
error_exit='warning',
raise_exception=True)
except Exception:
# put the result and the reference in the same space using a registration with ANTs with no iteration:
corrected_manual_gmseg = file_manual_gmseg + '_in_old_template_space' + ext_manual_gmseg
sct.run('isct_antsRegistration -d 3 -t Translation[0] -m MI[' +
fname_old_template_gm + ',' + file_manual_gmseg +
ext_manual_gmseg + ',1,16] -o [reg_ref_to_res,' +
corrected_manual_gmseg + '] -n BSpline[3] -c 0 -f 1 -s 0')
# sct.run('sct_maths -i '+corrected_manual_gmseg+' -thr 0.1 -o '+corrected_manual_gmseg)
sct.run('sct_maths -i ' + corrected_manual_gmseg +
' -bin 0.1 -o ' + corrected_manual_gmseg)
status_old_gm, output_old_gm = sct.run(
'sct_dice_coefficient -i ' + corrected_manual_gmseg + ' -d ' +
fname_old_template_gm + ' -2d-slices 2',
error_exit='warning')
try:
status_old_wm, output_old_wm = sct.run(
'sct_dice_coefficient -i ' + fname_manual_wmseg + ' -d ' +
fname_old_template_wm + ' -2d-slices 2',
error_exit='warning',
raise_exception=True)
except Exception:
# put the result and the reference in the same space using a registration with ANTs with no iteration:
path_manual_wmseg, file_manual_wmseg, ext_manual_wmseg = sct.extract_fname(
fname_manual_wmseg)
corrected_manual_wmseg = file_manual_wmseg + '_in_old_template_space' + ext_manual_wmseg
sct.run('isct_antsRegistration -d 3 -t Translation[0] -m MI[' +
fname_old_template_wm + ',' + fname_manual_wmseg +
',1,16] -o [reg_ref_to_res,' + corrected_manual_wmseg +
'] -n BSpline[3] -c 0 -f 1 -s 0')
# sct.run('sct_maths -i '+corrected_manual_wmseg+' -thr 0.1 -o '+corrected_manual_wmseg)
sct.run('sct_maths -i ' + corrected_manual_wmseg +
' -bin 0.1 -o ' + corrected_manual_wmseg)
status_old_wm, output_old_wm = sct.run(
'sct_dice_coefficient -i ' + corrected_manual_wmseg + ' -d ' +
fname_old_template_wm + ' -2d-slices 2',
error_exit='warning')
# --- DC new template
try:
status_new_gm, output_new_gm = sct.run(
'sct_dice_coefficient -i ' + file_manual_gmseg +
ext_manual_gmseg + ' -d ' + fname_new_template_gm +
' -2d-slices 2',
error_exit='warning',
raise_exception=True)
except Exception:
# put the result and the reference in the same space using a registration with ANTs with no iteration:
corrected_manual_gmseg = file_manual_gmseg + '_in_new_template_space' + ext_manual_gmseg
sct.run('isct_antsRegistration -d 3 -t Translation[0] -m MI[' +
fname_new_template_gm + ',' + file_manual_gmseg +
ext_manual_gmseg + ',1,16] -o [reg_ref_to_res,' +
corrected_manual_gmseg + '] -n BSpline[3] -c 0 -f 1 -s 0')
# sct.run('sct_maths -i '+corrected_manual_gmseg+' -thr 0.1 -o '+corrected_manual_gmseg)
sct.run('sct_maths -i ' + corrected_manual_gmseg +
' -bin 0.1 -o ' + corrected_manual_gmseg)
status_new_gm, output_new_gm = sct.run(
'sct_dice_coefficient -i ' + corrected_manual_gmseg + ' -d ' +
fname_new_template_gm + ' -2d-slices 2',
error_exit='warning')
try:
status_new_wm, output_new_wm = sct.run(
'sct_dice_coefficient -i ' + fname_manual_wmseg + ' -d ' +
fname_new_template_wm + ' -2d-slices 2',
error_exit='warning',
raise_exception=True)
except Exception:
# put the result and the reference in the same space using a registration with ANTs with no iteration:
path_manual_wmseg, file_manual_wmseg, ext_manual_wmseg = sct.extract_fname(
fname_manual_wmseg)
corrected_manual_wmseg = file_manual_wmseg + '_in_new_template_space' + ext_manual_wmseg
sct.run('isct_antsRegistration -d 3 -t Translation[0] -m MI[' +
fname_new_template_wm + ',' + fname_manual_wmseg +
',1,16] -o [reg_ref_to_res,' + corrected_manual_wmseg +
'] -n BSpline[3] -c 0 -f 1 -s 0')
# sct.run('sct_maths -i '+corrected_manual_wmseg+' -thr 0.1 -o '+corrected_manual_wmseg)
sct.run('sct_maths -i ' + corrected_manual_wmseg +
' -bin 0.1 -o ' + corrected_manual_wmseg)
status_new_wm, output_new_wm = sct.run(
'sct_dice_coefficient -i ' + corrected_manual_wmseg + ' -d ' +
fname_new_template_wm + ' -2d-slices 2',
error_exit='warning')
dice_name = 'dice_multilabel_reg.txt'
dice_fic = open(dice_name, 'w')
dice_fic.write(
'The "diff" columns are comparisons between regular template registration and corrected template registration according to SC internal structure\n'
'Diff = metric_corrected_reg - metric_regular_reg\n')
dice_fic.write('#Slice, WM DC, WM diff, GM DC, GM diff\n')
init_dc = '2D Dice coefficient by slice:\n'
old_gm_dc = output_old_gm[output_old_gm.find(init_dc) +
len(init_dc):].split('\n')
old_wm_dc = output_old_wm[output_old_wm.find(init_dc) +
len(init_dc):].split('\n')
new_gm_dc = output_new_gm[output_new_gm.find(init_dc) +
len(init_dc):].split('\n')
new_wm_dc = output_new_wm[output_new_wm.find(init_dc) +
len(init_dc):].split('\n')
for i in range(len(old_gm_dc)):
if i not in no_ref_slices:
i_new_gm, val_new_gm = new_gm_dc[i].split(' ')
i_new_wm, val_new_wm = new_wm_dc[i].split(' ')
i_old_gm, val_old_gm = old_gm_dc[i].split(' ')
i_old_wm, val_old_wm = old_wm_dc[i].split(' ')
assert i == int(i_new_gm) == int(i_new_wm) == int(
i_old_gm
) == int(
i_old_wm
), 'ERROR: when comparing Dice coefficients, slice numbers differs.'
dice_fic.write(
str(i) + ', ' + val_new_wm + ', ' +
str(float(val_new_wm) - float(val_old_wm)) + ', ' +
val_new_gm + ', ' +
str(float(val_new_gm) - float(val_old_gm)) + '\n')
else:
dice_fic.write(str(i) + ', NO MANUAL SEGMENTATION\n')
dice_fic.close()
os.chdir('..')
sct.generate_output_file(tmp_dir + hd_name,
self.param.output_folder + hd_name)
sct.generate_output_file(tmp_dir + dice_name,
self.param.output_folder + dice_name)
if self.param.remove_tmp:
sct.run('rm -rf ' + tmp_dir, error_exit='warning')
def main(args=None):
# Initialization
param = Param()
start_time = time.time()
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
fname_anat = arguments['-i']
fname_centerline = arguments['-s']
if '-smooth' in arguments:
sigma = arguments['-smooth']
if '-param' in arguments:
param.update(arguments['-param'])
if '-r' in arguments:
remove_temp_files = int(arguments['-r'])
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
# Display arguments
sct.printv('\nCheck input arguments...')
sct.printv(' Volume to smooth .................. ' + fname_anat)
sct.printv(' Centerline ........................ ' + fname_centerline)
sct.printv(' Sigma (mm) ........................ ' + str(sigma))
sct.printv(' Verbose ........................... ' + str(verbose))
# Check that input is 3D:
from spinalcordtoolbox.image import Image
nx, ny, nz, nt, px, py, pz, pt = Image(fname_anat).dim
dim = 4 # by default, will be adjusted later
if nt == 1:
dim = 3
if nz == 1:
dim = 2
if dim == 4:
sct.printv(
'WARNING: the input image is 4D, please split your image to 3D before smoothing spinalcord using :\n'
'sct_image -i ' + fname_anat + ' -split t -o ' + fname_anat,
verbose, 'warning')
sct.printv('4D images not supported, aborting ...', verbose, 'error')
# Extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
path_centerline, file_centerline, ext_centerline = sct.extract_fname(
fname_centerline)
path_tmp = sct.tmp_create(basename="smooth_spinalcord", verbose=verbose)
# Copying input data to tmp folder
sct.printv('\nCopying input data to tmp folder and convert to nii...',
verbose)
sct.copy(fname_anat, os.path.join(path_tmp, "anat" + ext_anat))
sct.copy(fname_centerline,
os.path.join(path_tmp, "centerline" + ext_centerline))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# convert to nii format
convert('anat' + ext_anat, 'anat.nii')
convert('centerline' + ext_centerline, 'centerline.nii')
# Change orientation of the input image into RPI
sct.printv('\nOrient input volume to RPI orientation...')
fname_anat_rpi = msct_image.Image("anat.nii") \
.change_orientation("RPI", generate_path=True) \
.save() \
.absolutepath
# Change orientation of the input image into RPI
sct.printv('\nOrient centerline to RPI orientation...')
fname_centerline_rpi = msct_image.Image("centerline.nii") \
.change_orientation("RPI", generate_path=True) \
.save() \
.absolutepath
# Straighten the spinal cord
# straighten segmentation
sct.printv('\nStraighten the spinal cord using centerline/segmentation...',
verbose)
cache_sig = sct.cache_signature(
input_files=[fname_anat_rpi, fname_centerline_rpi],
input_params={"x": "spline"})
cachefile = os.path.join(curdir, "straightening.cache")
if sct.cache_valid(cachefile, cache_sig) and os.path.isfile(
os.path.join(
curdir, 'warp_curve2straight.nii.gz')) and os.path.isfile(
os.path.join(
curdir,
'warp_straight2curve.nii.gz')) and os.path.isfile(
os.path.join(curdir, 'straight_ref.nii.gz')):
# if they exist, copy them into current folder
sct.printv('Reusing existing warping field which seems to be valid',
verbose, 'warning')
sct.copy(os.path.join(curdir, 'warp_curve2straight.nii.gz'),
'warp_curve2straight.nii.gz')
sct.copy(os.path.join(curdir, 'warp_straight2curve.nii.gz'),
'warp_straight2curve.nii.gz')
sct.copy(os.path.join(curdir, 'straight_ref.nii.gz'),
'straight_ref.nii.gz')
# apply straightening
sct.run([
'sct_apply_transfo', '-i', fname_anat_rpi, '-w',
'warp_curve2straight.nii.gz', '-d', 'straight_ref.nii.gz', '-o',
'anat_rpi_straight.nii', '-x', 'spline'
], verbose)
else:
sct.run([
'sct_straighten_spinalcord', '-i', fname_anat_rpi, '-o',
'anat_rpi_straight.nii', '-s', fname_centerline_rpi, '-x',
'spline', '-param', 'algo_fitting=' + param.algo_fitting
], verbose)
sct.cache_save(cachefile, cache_sig)
# move warping fields locally (to use caching next time)
sct.copy('warp_curve2straight.nii.gz',
os.path.join(curdir, 'warp_curve2straight.nii.gz'))
sct.copy('warp_straight2curve.nii.gz',
os.path.join(curdir, 'warp_straight2curve.nii.gz'))
# Smooth the straightened image along z
sct.printv('\nSmooth the straightened image...')
sigma_smooth = ",".join([str(i) for i in sigma])
sct_maths.main(args=[
'-i', 'anat_rpi_straight.nii', '-smooth', sigma_smooth, '-o',
'anat_rpi_straight_smooth.nii', '-v', '0'
])
# Apply the reversed warping field to get back the curved spinal cord
sct.printv(
'\nApply the reversed warping field to get back the curved spinal cord...'
)
sct.run([
'sct_apply_transfo', '-i', 'anat_rpi_straight_smooth.nii', '-o',
'anat_rpi_straight_smooth_curved.nii', '-d', 'anat.nii', '-w',
'warp_straight2curve.nii.gz', '-x', 'spline'
], verbose)
# replace zeroed voxels by original image (issue #937)
sct.printv('\nReplace zeroed voxels by original image...', verbose)
nii_smooth = Image('anat_rpi_straight_smooth_curved.nii')
data_smooth = nii_smooth.data
data_input = Image('anat.nii').data
indzero = np.where(data_smooth == 0)
data_smooth[indzero] = data_input[indzero]
nii_smooth.data = data_smooth
nii_smooth.save('anat_rpi_straight_smooth_curved_nonzero.nii')
# come back
os.chdir(curdir)
# Generate output file
sct.printv('\nGenerate output file...')
sct.generate_output_file(
os.path.join(path_tmp, "anat_rpi_straight_smooth_curved_nonzero.nii"),
file_anat + '_smooth' + ext_anat)
# Remove temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...')
sct.rmtree(path_tmp)
# Display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' +
str(int(np.round(elapsed_time))) + 's\n')
sct.display_viewer_syntax([file_anat, file_anat + '_smooth'],
verbose=verbose)
# append path that contains scripts, to be able to load modules
path_script = os.path.dirname(__file__)
# Get path of the toolbox
path_sct = os.environ.get("SCT_DIR", os.path.dirname(os.path.dirname(__file__)))
sys.path.append(os.path.join(path_sct, 'scripts'))
import sct_utils as sct
from msct_image import Image
# parameters
fname_wm = os.path.join(path_sct, "PAM50", "template", "PAM50_wm.nii.gz")
fname_gm = os.path.join(path_sct, "PAM50", "template", "PAM50_gm.nii.gz")
fname_cord = os.path.join(path_sct, "PAM50", "template", "PAM50_cord.nii.gz")
# create temporary folder
path_tmp = sct.tmp_create()
# go to temp folder
os.chdir(path_tmp)
# open volumes
im_wm = Image(fname_wm)
data_wm = im_wm.data
im_gm = Image(fname_gm)
data_gm = im_gm.data
im_cord = Image(fname_cord)
data_cord = im_cord.data
dim = im_cord.dim
# sum wm/gm
data_wmgm = data_wm + data_gm
def main(args=None):
# initializations
initz = ''
initcenter = ''
fname_initlabel = ''
file_labelz = 'labelz.nii.gz'
param = Param()
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(args)
fname_in = os.path.abspath(arguments["-i"])
fname_seg = os.path.abspath(arguments['-s'])
contrast = arguments['-c']
path_template = os.path.abspath(arguments['-t'])
scale_dist = arguments['-scale-dist']
if '-ofolder' in arguments:
path_output = arguments['-ofolder']
else:
path_output = os.curdir
param.path_qc = arguments.get("-qc", None)
if '-discfile' in arguments:
fname_disc = os.path.abspath(arguments['-discfile'])
else:
fname_disc = None
if '-initz' in arguments:
initz = arguments['-initz']
if '-initcenter' in arguments:
initcenter = arguments['-initcenter']
# if user provided text file, parse and overwrite arguments
if '-initfile' in arguments:
file = open(arguments['-initfile'], 'r')
initfile = ' ' + file.read().replace('\n', '')
arg_initfile = initfile.split(' ')
for idx_arg, arg in enumerate(arg_initfile):
if arg == '-initz':
initz = [int(x) for x in arg_initfile[idx_arg + 1].split(',')]
if arg == '-initcenter':
initcenter = int(arg_initfile[idx_arg + 1])
if '-initlabel' in arguments:
# get absolute path of label
fname_initlabel = os.path.abspath(arguments['-initlabel'])
if '-param' in arguments:
param.update(arguments['-param'][0])
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
remove_temp_files = int(arguments['-r'])
denoise = int(arguments['-denoise'])
laplacian = int(arguments['-laplacian'])
path_tmp = sct.tmp_create(basename="label_vertebrae", verbose=verbose)
# Copying input data to tmp folder
sct.printv('\nCopying input data to tmp folder...', verbose)
Image(fname_in).save(os.path.join(path_tmp, "data.nii"))
Image(fname_seg).save(os.path.join(path_tmp, "segmentation.nii"))
# Go go temp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Straighten spinal cord
sct.printv('\nStraighten spinal cord...', verbose)
# check if warp_curve2straight and warp_straight2curve already exist (i.e. no need to do it another time)
cache_sig = sct.cache_signature(input_files=[fname_in, fname_seg], )
cachefile = os.path.join(curdir, "straightening.cache")
if sct.cache_valid(cachefile, cache_sig) and os.path.isfile(
os.path.join(
curdir, "warp_curve2straight.nii.gz")) and os.path.isfile(
os.path.join(
curdir,
"warp_straight2curve.nii.gz")) and os.path.isfile(
os.path.join(curdir, "straight_ref.nii.gz")):
# if they exist, copy them into current folder
sct.printv('Reusing existing warping field which seems to be valid',
verbose, 'warning')
sct.copy(os.path.join(curdir, "warp_curve2straight.nii.gz"),
'warp_curve2straight.nii.gz')
sct.copy(os.path.join(curdir, "warp_straight2curve.nii.gz"),
'warp_straight2curve.nii.gz')
sct.copy(os.path.join(curdir, "straight_ref.nii.gz"),
'straight_ref.nii.gz')
# apply straightening
s, o = sct.run([
'sct_apply_transfo', '-i', 'data.nii', '-w',
'warp_curve2straight.nii.gz', '-d', 'straight_ref.nii.gz', '-o',
'data_straight.nii'
])
else:
sct_straighten_spinalcord.main(args=[
'-i',
'data.nii',
'-s',
'segmentation.nii',
'-r',
str(remove_temp_files),
'-v',
str(verbose),
])
sct.cache_save(cachefile, cache_sig)
# resample to 0.5mm isotropic to match template resolution
sct.printv('\nResample to 0.5mm isotropic...', verbose)
s, o = sct.run([
'sct_resample', '-i', 'data_straight.nii', '-mm', '0.5x0.5x0.5', '-x',
'linear', '-o', 'data_straightr.nii'
],
verbose=verbose)
# Apply straightening to segmentation
# N.B. Output is RPI
sct.printv('\nApply straightening to segmentation...', verbose)
sct.run(
'isct_antsApplyTransforms -d 3 -i %s -r %s -t %s -o %s -n %s' %
('segmentation.nii', 'data_straightr.nii',
'warp_curve2straight.nii.gz', 'segmentation_straight.nii', 'Linear'),
verbose=verbose,
is_sct_binary=True,
)
# Threshold segmentation at 0.5
sct.run([
'sct_maths', '-i', 'segmentation_straight.nii', '-thr', '0.5', '-o',
'segmentation_straight.nii'
], verbose)
# If disc label file is provided, label vertebrae using that file instead of automatically
if fname_disc:
# Apply straightening to disc-label
sct.printv('\nApply straightening to disc labels...', verbose)
sct.run(
'isct_antsApplyTransforms -d 3 -i %s -r %s -t %s -o %s -n %s' %
(fname_disc, 'data_straightr.nii', 'warp_curve2straight.nii.gz',
'labeldisc_straight.nii.gz', 'NearestNeighbor'),
verbose=verbose,
is_sct_binary=True,
)
label_vert('segmentation_straight.nii',
'labeldisc_straight.nii.gz',
verbose=1)
else:
# create label to identify disc
sct.printv('\nCreate label to identify disc...', verbose)
fname_labelz = os.path.join(path_tmp, file_labelz)
if initz or initcenter:
if initcenter:
# find z centered in FOV
nii = Image('segmentation.nii').change_orientation("RPI")
nx, ny, nz, nt, px, py, pz, pt = nii.dim # Get dimensions
z_center = int(np.round(nz / 2)) # get z_center
initz = [z_center, initcenter]
# create single label and output as labels.nii.gz
label = ProcessLabels(
'segmentation.nii',
fname_output='tmp.labelz.nii.gz',
coordinates=['{},{}'.format(initz[0], initz[1])])
im_label = label.process('create-seg')
im_label.data = dilate(
im_label.data, 3,
'ball') # TODO: create a dilation method specific to labels,
# which does not apply a convolution across all voxels (highly inneficient)
im_label.save(fname_labelz)
elif fname_initlabel:
Image(fname_initlabel).save(fname_labelz)
else:
# automatically finds C2-C3 disc
im_data = Image('data.nii')
im_seg = Image('segmentation.nii')
if not remove_temp_files: # because verbose is here also used for keeping temp files
verbose_detect_c2c3 = 2
else:
verbose_detect_c2c3 = 0
im_label_c2c3 = detect_c2c3(im_data,
im_seg,
contrast,
verbose=verbose_detect_c2c3)
ind_label = np.where(im_label_c2c3.data)
if not np.size(ind_label) == 0:
im_label_c2c3.data[ind_label] = 3
else:
sct.printv(
'Automatic C2-C3 detection failed. Please provide manual label with sct_label_utils',
1, 'error')
sys.exit()
im_label_c2c3.save(fname_labelz)
# dilate label so it is not lost when applying warping
dilate(Image(fname_labelz), 3, 'ball').save(fname_labelz)
# Apply straightening to z-label
sct.printv('\nAnd apply straightening to label...', verbose)
sct.run(
'isct_antsApplyTransforms -d 3 -i %s -r %s -t %s -o %s -n %s' %
(file_labelz, 'data_straightr.nii', 'warp_curve2straight.nii.gz',
'labelz_straight.nii.gz', 'NearestNeighbor'),
verbose=verbose,
is_sct_binary=True,
)
# get z value and disk value to initialize labeling
sct.printv('\nGet z and disc values from straight label...', verbose)
init_disc = get_z_and_disc_values_from_label('labelz_straight.nii.gz')
sct.printv('.. ' + str(init_disc), verbose)
# denoise data
if denoise:
sct.printv('\nDenoise data...', verbose)
sct.run([
'sct_maths', '-i', 'data_straightr.nii', '-denoise', 'h=0.05',
'-o', 'data_straightr.nii'
], verbose)
# apply laplacian filtering
if laplacian:
sct.printv('\nApply Laplacian filter...', verbose)
sct.run([
'sct_maths', '-i', 'data_straightr.nii', '-laplacian', '1',
'-o', 'data_straightr.nii'
], verbose)
# detect vertebral levels on straight spinal cord
init_disc[1] = init_disc[1] - 1
vertebral_detection('data_straightr.nii',
'segmentation_straight.nii',
contrast,
param,
init_disc=init_disc,
verbose=verbose,
path_template=path_template,
path_output=path_output,
scale_dist=scale_dist)
# un-straighten labeled spinal cord
sct.printv('\nUn-straighten labeling...', verbose)
sct.run(
'isct_antsApplyTransforms -d 3 -i %s -r %s -t %s -o %s -n %s' %
('segmentation_straight_labeled.nii', 'segmentation.nii',
'warp_straight2curve.nii.gz', 'segmentation_labeled.nii',
'NearestNeighbor'),
verbose=verbose,
is_sct_binary=True,
)
# Clean labeled segmentation
sct.printv(
'\nClean labeled segmentation (correct interpolation errors)...',
verbose)
clean_labeled_segmentation('segmentation_labeled.nii', 'segmentation.nii',
'segmentation_labeled.nii')
# label discs
sct.printv('\nLabel discs...', verbose)
label_discs('segmentation_labeled.nii', verbose=verbose)
# come back
os.chdir(curdir)
# Generate output files
path_seg, file_seg, ext_seg = sct.extract_fname(fname_seg)
fname_seg_labeled = os.path.join(path_output,
file_seg + '_labeled' + ext_seg)
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(
os.path.join(path_tmp, "segmentation_labeled.nii"), fname_seg_labeled)
sct.generate_output_file(
os.path.join(path_tmp, "segmentation_labeled_disc.nii"),
os.path.join(path_output, file_seg + '_labeled_discs' + ext_seg))
# copy straightening files in case subsequent SCT functions need them
sct.generate_output_file(
os.path.join(path_tmp, "warp_curve2straight.nii.gz"),
os.path.join(path_output, "warp_curve2straight.nii.gz"), verbose)
sct.generate_output_file(
os.path.join(path_tmp, "warp_straight2curve.nii.gz"),
os.path.join(path_output, "warp_straight2curve.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "straight_ref.nii.gz"),
os.path.join(path_output, "straight_ref.nii.gz"),
verbose)
# Remove temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...', verbose)
sct.rmtree(path_tmp)
# Generate QC report
if param.path_qc is not None:
path_qc = os.path.abspath(param.path_qc)
qc_dataset = arguments.get("-qc-dataset", None)
qc_subject = arguments.get("-qc-subject", None)
labeled_seg_file = os.path.join(path_output,
file_seg + '_labeled' + ext_seg)
generate_qc(fname_in,
fname_seg=labeled_seg_file,
args=args,
path_qc=os.path.abspath(path_qc),
dataset=qc_dataset,
subject=qc_subject,
process='sct_label_vertebrae')
sct.display_viewer_syntax([fname_in, fname_seg_labeled],
colormaps=['', 'subcortical'],
opacities=['1', '0.5'])
def apply(self):
# Initialization
fname_src = self.input_filename # source image (moving)
fname_warp_list = self.warp_input # list of warping fields
fname_out = self.output_filename # output
fname_dest = self.fname_dest # destination image (fix)
verbose = self.verbose
remove_temp_files = self.remove_temp_files
crop_reference = self.crop # if = 1, put 0 everywhere around warping field, if = 2, real crop
interp = sct.get_interpolation('isct_antsApplyTransforms', self.interp)
# Parse list of warping fields
sct.printv('\nParse list of warping fields...', verbose)
use_inverse = []
fname_warp_list_invert = []
# fname_warp_list = fname_warp_list.replace(' ', '') # remove spaces
# fname_warp_list = fname_warp_list.split(",") # parse with comma
for idx_warp, path_warp in enumerate(fname_warp_list):
# Check if inverse matrix is specified with '-' at the beginning of file name
if path_warp.startswith("-"):
use_inverse.append('-i')
fname_warp_list[idx_warp] = path_warp[1:] # remove '-'
fname_warp_list_invert += [[use_inverse[idx_warp], fname_warp_list[idx_warp]]]
else:
use_inverse.append('')
fname_warp_list_invert += [[path_warp]]
path_warp = fname_warp_list[idx_warp]
if path_warp.endswith((".nii", ".nii.gz")) \
and msct_image.Image(fname_warp_list[idx_warp]).header.get_intent()[0] != 'vector':
raise ValueError("Displacement field in {} is invalid: should be encoded" \
" in a 5D file with vector intent code" \
" (see https://nifti.nimh.nih.gov/pub/dist/src/niftilib/nifti1.h" \
.format(path_warp))
# need to check if last warping field is an affine transfo
isLastAffine = False
path_fname, file_fname, ext_fname = sct.extract_fname(fname_warp_list_invert[-1][-1])
if ext_fname in ['.txt', '.mat']:
isLastAffine = True
# check if destination file is 3d
if not sct.check_if_3d(fname_dest):
sct.printv('ERROR: Destination data must be 3d')
# N.B. Here we take the inverse of the warp list, because sct_WarpImageMultiTransform concatenates in the reverse order
fname_warp_list_invert.reverse()
fname_warp_list_invert = functools.reduce(lambda x,y: x+y, fname_warp_list_invert)
# Extract path, file and extension
path_src, file_src, ext_src = sct.extract_fname(fname_src)
path_dest, file_dest, ext_dest = sct.extract_fname(fname_dest)
# Get output folder and file name
if fname_out == '':
path_out = '' # output in user's current directory
file_out = file_src + '_reg'
ext_out = ext_src
fname_out = os.path.join(path_out, file_out + ext_out)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
img_src = msct_image.Image(fname_src)
nx, ny, nz, nt, px, py, pz, pt = img_src.dim
# nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(fname_src)
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt), verbose)
# if 3d
if nt == 1:
# Apply transformation
sct.printv('\nApply transformation...', verbose)
if nz in [0, 1]:
dim = '2'
else:
dim = '3'
sct.run(['isct_antsApplyTransforms',
'-d', dim,
'-i', fname_src,
'-o', fname_out,
'-t',
] + fname_warp_list_invert + [
'-r', fname_dest,
] + interp, verbose=verbose, is_sct_binary=True)
# if 4d, loop across the T dimension
else:
path_tmp = sct.tmp_create(basename="apply_transfo", verbose=verbose)
# convert to nifti into temp folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
img_src.save(os.path.join(path_tmp, "data.nii"))
sct.copy(fname_dest, os.path.join(path_tmp, file_dest + ext_dest))
fname_warp_list_tmp = []
for fname_warp in fname_warp_list:
path_warp, file_warp, ext_warp = sct.extract_fname(fname_warp)
sct.copy(fname_warp, os.path.join(path_tmp, file_warp + ext_warp))
fname_warp_list_tmp.append(file_warp + ext_warp)
fname_warp_list_invert_tmp = fname_warp_list_tmp[::-1]
curdir = os.getcwd()
os.chdir(path_tmp)
# split along T dimension
sct.printv('\nSplit along T dimension...', verbose)
im_dat = msct_image.Image('data.nii')
im_header = im_dat.hdr
data_split_list = sct_image.split_data(im_dat, 3)
for im in data_split_list:
im.save()
# apply transfo
sct.printv('\nApply transformation to each 3D volume...', verbose)
for it in range(nt):
file_data_split = 'data_T' + str(it).zfill(4) + '.nii'
file_data_split_reg = 'data_reg_T' + str(it).zfill(4) + '.nii'
status, output = sct.run(['isct_antsApplyTransforms',
'-d', '3',
'-i', file_data_split,
'-o', file_data_split_reg,
'-t',
] + fname_warp_list_invert_tmp + [
'-r', file_dest + ext_dest,
] + interp, verbose, is_sct_binary=True)
# Merge files back
sct.printv('\nMerge file back...', verbose)
import glob
path_out, name_out, ext_out = sct.extract_fname(fname_out)
# im_list = [Image(file_name) for file_name in glob.glob('data_reg_T*.nii')]
# concat_data use to take a list of image in input, now takes a list of file names to open the files one by one (see issue #715)
fname_list = glob.glob('data_reg_T*.nii')
fname_list.sort()
im_out = sct_image.concat_data(fname_list, 3, im_header['pixdim'])
im_out.save(name_out + ext_out)
os.chdir(curdir)
sct.generate_output_file(os.path.join(path_tmp, name_out + ext_out), fname_out)
# Delete temporary folder if specified
if int(remove_temp_files):
sct.printv('\nRemove temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# 2. crop the resulting image using dimensions from the warping field
warping_field = fname_warp_list_invert[-1]
# if last warping field is an affine transfo, we need to compute the space of the concatenate warping field:
if isLastAffine:
sct.printv('WARNING: the resulting image could have wrong apparent results. You should use an affine transformation as last transformation...', verbose, 'warning')
elif crop_reference == 1:
ImageCropper(input_file=fname_out, output_file=fname_out, ref=warping_field, background=0).crop()
# sct.run('sct_crop_image -i '+fname_out+' -o '+fname_out+' -ref '+warping_field+' -b 0')
elif crop_reference == 2:
ImageCropper(input_file=fname_out, output_file=fname_out, ref=warping_field).crop()
# sct.run('sct_crop_image -i '+fname_out+' -o '+fname_out+' -ref '+warping_field)
sct.display_viewer_syntax([fname_dest, fname_out], verbose=verbose)
def main():
# Initialization
fname_anat = ''
fname_centerline = ''
gapxy = param.gapxy
gapz = param.gapz
padding = param.padding
centerline_fitting = param.fitting_method
remove_temp_files = param.remove_temp_files
verbose = param.verbose
interpolation_warp = param.interpolation_warp
# get path of the toolbox
path_sct = os.environ.get("SCT_DIR",
os.path.dirname(os.path.dirname(__file__)))
print path_sct
# extract path of the script
path_script = os.path.dirname(__file__) + '/'
# Parameters for debug mode
if param.debug == 1:
print '\n*** WARNING: DEBUG MODE ON ***\n'
# fname_anat = path_sct+'/testing/data/errsm_23/t2/t2.nii.gz'
# fname_centerline = path_sct+'/testing/data/errsm_23/t2/t2_segmentation_PropSeg.nii.gz'
fname_anat = '/home/django/jtouati/data/cover_z_slices/errsm13_t2.nii.gz'
fname_centerline = '/home/django/jtouati/data/cover_z_slices/segmentation_centerline_binary.nii.gz'
remove_temp_files = 0
centerline_fitting = 'splines'
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
verbose = 2
# Check input param
try:
opts, args = getopt.getopt(sys.argv[1:], 'hi:c:r:w:f:v:')
except getopt.GetoptError as err:
print str(err)
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-i'):
fname_anat = arg
elif opt in ('-c'):
fname_centerline = arg
elif opt in ('-r'):
remove_temp_files = int(arg)
elif opt in ('-w'):
interpolation_warp = str(arg)
elif opt in ('-f'):
centerline_fitting = str(arg)
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname_anat == '' or fname_centerline == '':
usage()
# Display usage if optional arguments are not correctly provided
if centerline_fitting == '':
centerline_fitting = 'splines'
elif not centerline_fitting == '' and not centerline_fitting == 'splines' and not centerline_fitting == 'polynomial':
print '\n \n -f argument is not valid \n \n'
usage()
# check existence of input files
sct.check_file_exist(fname_anat)
sct.check_file_exist(fname_centerline)
# check interp method
if interpolation_warp == 'spline':
interpolation_warp_ants = '--use-BSpline'
elif interpolation_warp == 'trilinear':
interpolation_warp_ants = ''
elif interpolation_warp == 'nearestneighbor':
interpolation_warp_ants = '--use-NN'
else:
print '\WARNING: Interpolation method not recognized. Using: ' + param.interpolation_warp
interpolation_warp_ants = '--use-BSpline'
# Display arguments
print '\nCheck input arguments...'
print ' Input volume ...................... ' + fname_anat
print ' Centerline ........................ ' + fname_centerline
print ' Centerline fitting option ......... ' + centerline_fitting
print ' Final interpolation ............... ' + interpolation_warp
print ' Verbose ........................... ' + str(verbose)
print ''
# if verbose 2, import matplotlib
if verbose == 2:
import matplotlib.pyplot as plt
# Extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
path_centerline, file_centerline, ext_centerline = sct.extract_fname(
fname_centerline)
path_tmp = sct.tmp_create(basename="straighten", verbose=verbose)
# copy files into tmp folder
sct.run('cp ' + fname_anat + ' ' + path_tmp)
sct.run('cp ' + fname_centerline + ' ' + path_tmp)
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Open centerline
#==========================================================================================
# Change orientation of the input centerline into RPI
print '\nOrient centerline to RPI orientation...'
fname_centerline_orient = 'tmp.centerline_rpi' + ext_centerline
sct.run('sct_orientation -i ' + file_centerline + ext_centerline + ' -o ' +
fname_centerline_orient + ' -orientation RPI')
print '\nGet dimensions of input centerline...'
nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(fname_centerline_orient)
print '.. matrix size: ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz)
print '.. voxel size: ' + str(px) + 'mm x ' + str(py) + 'mm x ' + str(
pz) + 'mm'
print '\nOpen centerline volume...'
file = nibabel.load(fname_centerline_orient)
data = file.get_data()
# loop across z and associate x,y coordinate with the point having maximum intensity
x_centerline = [0 for iz in range(0, nz, 1)]
y_centerline = [0 for iz in range(0, nz, 1)]
z_centerline = [iz for iz in range(0, nz, 1)]
x_centerline_deriv = [0 for iz in range(0, nz, 1)]
y_centerline_deriv = [0 for iz in range(0, nz, 1)]
z_centerline_deriv = [0 for iz in range(0, nz, 1)]
# Two possible scenario:
# 1. the centerline is probabilistic: each slice contains voxels with the probability of containing the centerline [0:...:1]
# We only take the maximum value of the image to aproximate the centerline.
# 2. The centerline/segmentation image contains many pixels per slice with values {0,1}.
# We take all the points and approximate the centerline on all these points.
#
# x_seg_start, y_seg_start = (data[:,:,0]>0).nonzero()
# x_seg_end, y_seg_end = (data[:,:,-1]>0).nonzero()
# REMOVED: 2014-07-18
# check if centerline covers all the image
# if len(x_seg_start)==0 or len(x_seg_end)==0:
# print '\nERROR: centerline/segmentation must cover all "z" slices of the input image.\n' \
# 'To solve the problem, you need to crop the input image (you can use \'sct_crop_image\') and generate one' \
# 'more time the spinal cord centerline/segmentation from this cropped image.\n'
# usage()
#
# X, Y, Z = ((data<1)*(data>0)).nonzero() # X is empty if binary image
# if (len(X) > 0): # Scenario 1
# for iz in range(0, nz, 1):
# x_centerline[iz], y_centerline[iz] = numpy.unravel_index(data[:,:,iz].argmax(), data[:,:,iz].shape)
# else: # Scenario 2
# for iz in range(0, nz, 1):
# print (data[:,:,iz]>0).nonzero()
# x_seg, y_seg = (data[:,:,iz]>0).nonzero()
# x_centerline[iz] = numpy.mean(x_seg)
# y_centerline[iz] = numpy.mean(y_seg)
# # TODO: find a way to do the previous loop with this, which is more neat:
# # [numpy.unravel_index(data[:,:,iz].argmax(), data[:,:,iz].shape) for iz in range(0,nz,1)]
# get center of mass of the centerline/segmentation
print '\nGet center of mass of the centerline/segmentation...'
for iz in range(0, nz, 1):
x_centerline[iz], y_centerline[
iz] = ndimage.measurements.center_of_mass(
numpy.array(data[:, :, iz]))
#print len(x_centerline),len(y_centerline)
#print len((numpy.array(x_centerline)>=0).nonzero()[0]),len((numpy.array(y_centerline)>=0).nonzero()[0])
x_seg_start, y_seg_start = (data[:, :, 0] > 0).nonzero()
x_seg_end, y_seg_end = (data[:, :, -1] > 0).nonzero()
#check if centerline covers all the image
if len(x_seg_start) == 0 or len(x_seg_end) == 0:
sct.printv(
'\nWARNING : the centerline/segmentation you gave does not cover all "z" slices of the input image. Results should be improved if you crop the input image (you can use \'sct_crop_image\') and generate a new spinalcord centerline/segmentation from this cropped image.\n',
1, 'warning')
# print '\nWARNING : the centerline/segmentation you gave does not cover all "z" slices of the input image.\n' \
# 'Results should be improved if you crop the input image (you can use \'sct_crop_image\') and generate\n'\
# 'a new spinalcord centerline/segmentation from this cropped image.\n'
#print len((numpy.array(x_centerline)>=0).nonzero()[0]),len((numpy.array(y_centerline)>=0).nonzero()[0])
min_centerline = min((numpy.array(x_centerline) >= 0).nonzero()[0])
max_centerline = max((numpy.array(x_centerline) >= 0).nonzero()[0])
z_centerline = z_centerline[(min_centerline):(max_centerline + 1)]
#print len(z_centerline)
nz = len(z_centerline)
x_centerline = [x for x in x_centerline if not isnan(x)]
y_centerline = [y for y in y_centerline if not isnan(y)]
#print len(x_centerline),len(y_centerline)
# clear variable
del data
# Fit the centerline points with the kind of curve given as argument of the script and return the new fitted coordinates
if centerline_fitting == 'splines':
x_centerline_fit, y_centerline_fit, x_centerline_deriv, y_centerline_deriv, z_centerline_deriv = msct_smooth.b_spline_nurbs(
x_centerline, y_centerline, z_centerline, path_qc=curdir)
#x_centerline_fit, y_centerline_fit, x_centerline_deriv, y_centerline_deriv, z_centerline_deriv = b_spline_centerline(x_centerline,y_centerline,z_centerline)
elif centerline_fitting == 'polynomial':
x_centerline_fit, y_centerline_fit, polyx, polyy = polynome_centerline(
x_centerline, y_centerline, z_centerline)
#numpy.interp([i for i in xrange(0,min_centerline+1)],
#y_centerline_fit
#print z_centerline
if verbose == 2:
# plot centerline
ax = plt.subplot(1, 2, 1)
plt.plot(x_centerline, z_centerline, 'b:', label='centerline')
plt.plot(x_centerline_fit, z_centerline, 'r-', label='fit')
plt.xlabel('x')
plt.ylabel('z')
ax = plt.subplot(1, 2, 2)
plt.plot(y_centerline, z_centerline, 'b:', label='centerline')
plt.plot(y_centerline_fit, z_centerline, 'r-', label='fit')
plt.xlabel('y')
plt.ylabel('z')
handles, labels = ax.get_legend_handles_labels()
ax.legend(handles, labels)
plt.show()
# Get coordinates of landmarks along curved centerline
#==========================================================================================
print '\nGet coordinates of landmarks along curved centerline...'
# landmarks are created along the curved centerline every z=gapz. They consist of a "cross" of size gapx and gapy.
# find derivative of polynomial
step_z = round(nz / gapz)
#iz_curved = [i for i in range (0, nz, gapz)]
iz_curved = [(min(z_centerline) + i * step_z) for i in range(0, gapz)]
iz_curved.append(max(z_centerline))
#print iz_curved, len(iz_curved)
n_iz_curved = len(iz_curved)
#print n_iz_curved
landmark_curved = [[[0 for i in range(0, 3)] for i in range(0, 5)]
for i in iz_curved]
# print x_centerline_deriv,len(x_centerline_deriv)
# landmark[a][b][c]
# a: index along z. E.g., the first cross with have index=0, the next index=1, and so on...
# b: index of element on the cross. I.e., 0: center of the cross, 1: +x, 2 -x, 3: +y, 4: -y
# c: dimension, i.e., 0: x, 1: y, 2: z
# loop across index, which corresponds to iz (points along the centerline)
if centerline_fitting == 'polynomial':
for index in range(0, n_iz_curved, 1):
# set coordinates for landmark at the center of the cross
landmark_curved[index][0][0], landmark_curved[index][0][
1], landmark_curved[index][0][2] = x_centerline_fit[
iz_curved[index]], y_centerline_fit[
iz_curved[index]], iz_curved[index]
# set x and z coordinates for landmarks +x and -x
landmark_curved[index][1][2], landmark_curved[index][1][
0], landmark_curved[index][2][2], landmark_curved[index][2][
0] = get_points_perpendicular_to_curve(
polyx, polyx.deriv(), iz_curved[index], gapxy)
# set y coordinate to y_centerline_fit[iz] for elements 1 and 2 of the cross
for i in range(1, 3):
landmark_curved[index][i][1] = y_centerline_fit[
iz_curved[index]]
# set coordinates for landmarks +y and -y. Here, x coordinate is 0 (already initialized).
landmark_curved[index][3][2], landmark_curved[index][3][
1], landmark_curved[index][4][2], landmark_curved[index][4][
1] = get_points_perpendicular_to_curve(
polyy, polyy.deriv(), iz_curved[index], gapxy)
# set x coordinate to x_centerline_fit[iz] for elements 3 and 4 of the cross
for i in range(3, 5):
landmark_curved[index][i][0] = x_centerline_fit[
iz_curved[index]]
elif centerline_fitting == 'splines':
for index in range(0, n_iz_curved, 1):
# calculate d (ax+by+cz+d=0)
# print iz_curved[index]
a = x_centerline_deriv[iz_curved[index] - min(z_centerline)]
b = y_centerline_deriv[iz_curved[index] - min(z_centerline)]
c = z_centerline_deriv[iz_curved[index] - min(z_centerline)]
x = x_centerline_fit[iz_curved[index] - min(z_centerline)]
y = y_centerline_fit[iz_curved[index] - min(z_centerline)]
z = iz_curved[index]
d = -(a * x + b * y + c * z)
#print a,b,c,d,x,y,z
# set coordinates for landmark at the center of the cross
landmark_curved[index][0][0], landmark_curved[index][0][
1], landmark_curved[index][0][2] = x_centerline_fit[
iz_curved[index] - min(z_centerline)], y_centerline_fit[
iz_curved[index] - min(z_centerline)], iz_curved[index]
# set y coordinate to y_centerline_fit[iz] for elements 1 and 2 of the cross
for i in range(1, 3):
landmark_curved[index][i][1] = y_centerline_fit[
iz_curved[index] - min(z_centerline)]
# set x and z coordinates for landmarks +x and -x, forcing de landmark to be in the orthogonal plan and the distance landmark/curve to be gapxy
x_n = Symbol('x_n')
landmark_curved[index][2][0], landmark_curved[index][1][0] = solve(
(x_n - x)**2 + ((-1 / c) * (a * x_n + b * y + d) - z)**2 -
gapxy**2, x_n) #x for -x and +x
landmark_curved[index][1][2] = (-1 / c) * (
a * landmark_curved[index][1][0] + b * y + d) #z for +x
landmark_curved[index][2][2] = (-1 / c) * (
a * landmark_curved[index][2][0] + b * y + d) #z for -x
# set x coordinate to x_centerline_fit[iz] for elements 3 and 4 of the cross
for i in range(3, 5):
landmark_curved[index][i][0] = x_centerline_fit[
iz_curved[index] - min(z_centerline)]
# set coordinates for landmarks +y and -y. Here, x coordinate is 0 (already initialized).
y_n = Symbol('y_n')
landmark_curved[index][4][1], landmark_curved[index][3][1] = solve(
(y_n - y)**2 + ((-1 / c) * (a * x + b * y_n + d) - z)**2 -
gapxy**2, y_n) #y for -y and +y
landmark_curved[index][3][2] = (-1 / c) * (
a * x + b * landmark_curved[index][3][1] + d) #z for +y
landmark_curved[index][4][2] = (-1 / c) * (
a * x + b * landmark_curved[index][4][1] + d) #z for -y
# #display
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
# ax.plot(x_centerline_fit, y_centerline_fit,z_centerline, 'g')
# ax.plot(x_centerline, y_centerline,z_centerline, 'r')
# ax.plot([landmark_curved[i][j][0] for i in range(0, n_iz_curved) for j in range(0, 5)], \
# [landmark_curved[i][j][1] for i in range(0, n_iz_curved) for j in range(0, 5)], \
# [landmark_curved[i][j][2] for i in range(0, n_iz_curved) for j in range(0, 5)], '.')
# ax.set_xlabel('x')
# ax.set_ylabel('y')
# ax.set_zlabel('z')
# plt.show()
# Get coordinates of landmarks along straight centerline
#==========================================================================================
print '\nGet coordinates of landmarks along straight centerline...'
landmark_straight = [[[0 for i in range(0, 3)] for i in range(0, 5)]
for i in iz_curved
] # same structure as landmark_curved
# calculate the z indices corresponding to the Euclidean distance between two consecutive points on the curved centerline (approximation curve --> line)
iz_straight = [(min(z_centerline) + 0) for i in range(0, gapz + 1)]
#print iz_straight,len(iz_straight)
for index in range(1, n_iz_curved, 1):
# compute vector between two consecutive points on the curved centerline
vector_centerline = [x_centerline_fit[iz_curved[index]-min(z_centerline)] - x_centerline_fit[iz_curved[index-1]-min(z_centerline)], \
y_centerline_fit[iz_curved[index]-min(z_centerline)] - y_centerline_fit[iz_curved[index-1]-min(z_centerline)], \
iz_curved[index] - iz_curved[index-1]]
# compute norm of this vector
norm_vector_centerline = numpy.linalg.norm(vector_centerline, ord=2)
# round to closest integer value
norm_vector_centerline_rounded = int(round(norm_vector_centerline, 0))
# assign this value to the current z-coordinate on the straight centerline
iz_straight[index] = iz_straight[index -
1] + norm_vector_centerline_rounded
# initialize x0 and y0 to be at the center of the FOV
x0 = int(round(nx / 2))
y0 = int(round(ny / 2))
for index in range(0, n_iz_curved, 1):
# set coordinates for landmark at the center of the cross
landmark_straight[index][0][0], landmark_straight[index][0][
1], landmark_straight[index][0][2] = x0, y0, iz_straight[index]
# set x, y and z coordinates for landmarks +x
landmark_straight[index][1][0], landmark_straight[index][1][
1], landmark_straight[index][1][2] = x0 + gapxy, y0, iz_straight[
index]
# set x, y and z coordinates for landmarks -x
landmark_straight[index][2][0], landmark_straight[index][2][
1], landmark_straight[index][2][2] = x0 - gapxy, y0, iz_straight[
index]
# set x, y and z coordinates for landmarks +y
landmark_straight[index][3][0], landmark_straight[index][3][
1], landmark_straight[index][3][2] = x0, y0 + gapxy, iz_straight[
index]
# set x, y and z coordinates for landmarks -y
landmark_straight[index][4][0], landmark_straight[index][4][
1], landmark_straight[index][4][2] = x0, y0 - gapxy, iz_straight[
index]
# # display
# fig = plt.figure()
# ax = fig.add_subplot(111, projection='3d')
# #ax.plot(x_centerline_fit, y_centerline_fit,z_centerline, 'r')
# ax.plot([landmark_straight[i][j][0] for i in range(0, n_iz_curved) for j in range(0, 5)], \
# [landmark_straight[i][j][1] for i in range(0, n_iz_curved) for j in range(0, 5)], \
# [landmark_straight[i][j][2] for i in range(0, n_iz_curved) for j in range(0, 5)], '.')
# ax.set_xlabel('x')
# ax.set_ylabel('y')
# ax.set_zlabel('z')
# plt.show()
#
# Create NIFTI volumes with landmarks
#==========================================================================================
# Pad input volume to deal with the fact that some landmarks on the curved centerline might be outside the FOV
# N.B. IT IS VERY IMPORTANT TO PAD ALSO ALONG X and Y, OTHERWISE SOME LANDMARKS MIGHT GET OUT OF THE FOV!!!
print '\nPad input volume to deal with the fact that some landmarks on the curved centerline might be outside the FOV...'
sct.run('isct_c3d ' + fname_centerline_orient + ' -pad ' + str(padding) +
'x' + str(padding) + 'x' + str(padding) + 'vox ' + str(padding) +
'x' + str(padding) + 'x' + str(padding) +
'vox 0 -o tmp.centerline_pad.nii.gz')
# TODO: don't pad input volume: no need for that! instead, try to increase size of hdr when saving landmarks.
# Open padded centerline for reading
print '\nOpen padded centerline for reading...'
file = nibabel.load('tmp.centerline_pad.nii.gz')
data = file.get_data()
hdr = file.get_header()
# Create volumes containing curved and straight landmarks
data_curved_landmarks = data * 0
data_straight_landmarks = data * 0
# initialize landmark value
landmark_value = 1
# Loop across cross index
for index in range(0, n_iz_curved, 1):
# loop across cross element index
for i_element in range(0, 5, 1):
# get x, y and z coordinates of curved landmark (rounded to closest integer)
x, y, z = int(round(landmark_curved[index][i_element][0])), int(
round(landmark_curved[index][i_element][1])), int(
round(landmark_curved[index][i_element][2]))
# attribute landmark_value to the voxel and its neighbours
data_curved_landmarks[x + padding - 1:x + padding + 2,
y + padding - 1:y + padding + 2, z +
padding - 1:z + padding + 2] = landmark_value
# get x, y and z coordinates of straight landmark (rounded to closest integer)
x, y, z = int(round(landmark_straight[index][i_element][0])), int(
round(landmark_straight[index][i_element][1])), int(
round(landmark_straight[index][i_element][2]))
# attribute landmark_value to the voxel and its neighbours
data_straight_landmarks[x + padding - 1:x + padding + 2,
y + padding - 1:y + padding + 2,
z + padding - 1:z + padding +
2] = landmark_value
# increment landmark value
landmark_value = landmark_value + 1
# Write NIFTI volumes
hdr.set_data_dtype(
'uint32') # set imagetype to uint8 #TODO: maybe use int32
print '\nWrite NIFTI volumes...'
img = nibabel.Nifti1Image(data_curved_landmarks, None, hdr)
nibabel.save(img, 'tmp.landmarks_curved.nii.gz')
print '.. File created: tmp.landmarks_curved.nii.gz'
img = nibabel.Nifti1Image(data_straight_landmarks, None, hdr)
nibabel.save(img, 'tmp.landmarks_straight.nii.gz')
print '.. File created: tmp.landmarks_straight.nii.gz'
# Estimate deformation field by pairing landmarks
#==========================================================================================
# Dilate landmarks (because nearest neighbour interpolation will be later used, therefore some landmarks may "disapear" if they are single points)
#print '\nDilate landmarks...'
#sct.run(fsloutput+'fslmaths tmp.landmarks_curved.nii -kernel box 3x3x3 -dilD tmp.landmarks_curved_dilated -odt short')
#sct.run(fsloutput+'fslmaths tmp.landmarks_straight.nii -kernel box 3x3x3 -dilD tmp.landmarks_straight_dilated -odt short')
# Estimate rigid transformation
print '\nEstimate rigid transformation between paired landmarks...'
sct.run(
'isct_ANTSUseLandmarkImagesToGetAffineTransform tmp.landmarks_straight.nii.gz tmp.landmarks_curved.nii.gz rigid tmp.curve2straight_rigid.txt'
)
# Apply rigid transformation
print '\nApply rigid transformation to curved landmarks...'
sct.run(
'sct_WarpImageMultiTransform 3 tmp.landmarks_curved.nii.gz tmp.landmarks_curved_rigid.nii.gz -R tmp.landmarks_straight.nii.gz tmp.curve2straight_rigid.txt --use-NN'
)
# Estimate b-spline transformation curve --> straight
print '\nEstimate b-spline transformation: curve --> straight...'
sct.run(
'isct_ANTSUseLandmarkImagesToGetBSplineDisplacementField tmp.landmarks_straight.nii.gz tmp.landmarks_curved_rigid.nii.gz tmp.warp_curve2straight.nii.gz 5x5x5 3 2 0'
)
# Concatenate rigid and non-linear transformations...
print '\nConcatenate rigid and non-linear transformations...'
#sct.run('isct_ComposeMultiTransform 3 tmp.warp_rigid.nii -R tmp.landmarks_straight.nii tmp.warp.nii tmp.curve2straight_rigid.txt')
# TODO: use sct.run() when output from the following command will be different from 0 (currently there seem to be a bug)
cmd = 'isct_ComposeMultiTransform 3 tmp.curve2straight.nii.gz -R tmp.landmarks_straight.nii.gz tmp.warp_curve2straight.nii.gz tmp.curve2straight_rigid.txt'
print('>> ' + cmd)
sct.run(cmd)
# Estimate b-spline transformation straight --> curve
# TODO: invert warping field instead of estimating a new one
print '\nEstimate b-spline transformation: straight --> curve...'
sct.run(
'isct_ANTSUseLandmarkImagesToGetBSplineDisplacementField tmp.landmarks_curved_rigid.nii.gz tmp.landmarks_straight.nii.gz tmp.warp_straight2curve.nii.gz 5x5x5 3 2 0'
)
# Concatenate rigid and non-linear transformations...
print '\nConcatenate rigid and non-linear transformations...'
#sct.run('isct_ComposeMultiTransform 3 tmp.warp_rigid.nii -R tmp.landmarks_straight.nii tmp.warp.nii tmp.curve2straight_rigid.txt')
# TODO: use sct.run() when output from the following command will be different from 0 (currently there seem to be a bug)
cmd = 'isct_ComposeMultiTransform 3 tmp.straight2curve.nii.gz -R tmp.landmarks_straight.nii.gz -i tmp.curve2straight_rigid.txt tmp.warp_straight2curve.nii.gz'
print('>> ' + cmd)
sct.run(cmd)
#print '\nPad input image...'
#sct.run('isct_c3d '+fname_anat+' -pad '+str(padz)+'x'+str(padz)+'x'+str(padz)+'vox '+str(padz)+'x'+str(padz)+'x'+str(padz)+'vox 0 -o tmp.anat_pad.nii')
# Unpad landmarks...
# THIS WAS REMOVED ON 2014-06-03 because the output data was cropped at the edge, which caused landmarks to sometimes disappear
# print '\nUnpad landmarks...'
# sct.run('fslroi tmp.landmarks_straight.nii.gz tmp.landmarks_straight_crop.nii.gz '+str(padding)+' '+str(nx)+' '+str(padding)+' '+str(ny)+' '+str(padding)+' '+str(nz))
# Apply deformation to input image
print '\nApply transformation to input image...'
sct.run('sct_WarpImageMultiTransform 3 ' + file_anat + ext_anat +
' tmp.anat_rigid_warp.nii.gz -R tmp.landmarks_straight.nii.gz ' +
interpolation_warp + ' tmp.curve2straight.nii.gz')
# sct.run('sct_WarpImageMultiTransform 3 '+fname_anat+' tmp.anat_rigid_warp.nii.gz -R tmp.landmarks_straight_crop.nii.gz '+interpolation_warp+ ' tmp.curve2straight.nii.gz')
# come back
os.chdir(curdir)
# Generate output file (in current folder)
# TODO: do not uncompress the warping field, it is too time consuming!
print '\nGenerate output file (in current folder)...'
sct.generate_output_file(path_tmp + '/tmp.curve2straight.nii.gz', '',
'warp_curve2straight', '.nii.gz') # warping field
sct.generate_output_file(path_tmp + '/tmp.straight2curve.nii.gz', '',
'warp_straight2curve', '.nii.gz') # warping field
sct.generate_output_file(path_tmp + '/tmp.anat_rigid_warp.nii.gz', '',
file_anat + '_straight',
ext_anat) # straightened anatomic
# Remove temporary files
if remove_temp_files == 1:
print('\nRemove temporary files...')
sct.run('rm -rf ' + path_tmp)
print '\nDone!\n'
def test_function(param_test):
"""
Parameters
----------
file_testing
Returns
-------
path_output str: path where to output testing data
"""
# load modules of function to test
module_function_to_test = importlib.import_module(
param_test.function_to_test)
module_testing = importlib.import_module('test_' +
param_test.function_to_test)
# retrieve subject name
subject_folder = os.path.basename(param_test.path_data)
# build path_output variable
path_testing = os.getcwd()
if not param_test.path_output:
param_test.path_output = sct.tmp_create(
basename=(param_test.function_to_test + '_' + subject_folder),
verbose=0)
elif not os.path.isdir(param_test.path_output):
os.makedirs(param_test.path_output)
# get parser information
parser = module_function_to_test.get_parser()
if '-ofolder' in parser.options and '-ofolder' not in param_test.args:
param_test.args += " -ofolder " + param_test.path_output
dict_args = parser.parse(shlex.split(param_test.args),
check_file_exist=False)
# TODO: if file in list does not exist, raise exception and assign status=200
# add data path to each input argument
dict_args_with_path = parser.add_path_to_file(copy.deepcopy(dict_args),
param_test.path_data,
input_file=True)
# add data path to each output argument
dict_args_with_path = parser.add_path_to_file(
copy.deepcopy(dict_args_with_path),
param_test.path_output,
input_file=False,
output_file=True)
# save into class
param_test.dict_args_with_path = dict_args_with_path
param_test.args_with_path = parser.dictionary_to_string(
dict_args_with_path)
# initialize panda dataframe
param_test.results = DataFrame(index=[subject_folder],
data={
'status': 0,
'duration': 0,
'output': '',
'path_data': param_test.path_data,
'path_output': param_test.path_output
})
# retrieve input file (will be used later for integrity testing)
if '-i' in dict_args:
# check if list in case of multiple input files
if not isinstance(dict_args_with_path['-i'], list):
list_file_to_check = [dict_args_with_path['-i']]
# assign field file_input for integrity testing
param_test.file_input = dict_args['-i'].split('/')[-1]
# update index of dataframe by appending file name for more clarity
param_test.results = param_test.results.rename({
subject_folder:
os.path.join(subject_folder, dict_args['-i'])
})
else:
list_file_to_check = dict_args_with_path['-i']
# TODO: assign field file_input for integrity testing
for file_to_check in list_file_to_check:
# file_input = file_to_check.split('/')[1]
# Check if input files exist
if not (os.path.isfile(file_to_check)):
param_test.status = 200
param_test.output += '\nERROR: This input file does not exist: ' + file_to_check
return update_param(param_test)
# retrieve ground truth (will be used later for integrity testing)
if '-igt' in dict_args:
param_test.fname_gt = dict_args_with_path['-igt']
# Check if ground truth files exist
if not os.path.isfile(param_test.fname_gt):
param_test.status = 201
param_test.output += '\nERROR: The following file used for ground truth does not exist: ' + param_test.fname_gt
return update_param(param_test)
# run command
cmd = param_test.function_to_test + param_test.args_with_path
param_test.output += '\nWill run in %s:' % (os.path.join(
path_testing, param_test.path_output))
param_test.output += '\n====================================================================================================\n' + cmd + '\n====================================================================================================\n\n' # copy command
time_start = time.time()
try:
os.chdir(param_test.path_output)
if not os.path.exists(param_test.path_output):
# in case of relative path, we want a subfolder too
os.makedirs(param_test.path_output)
os.chdir(path_testing)
param_test.status, o = sct.run(cmd,
cwd=param_test.path_output,
verbose=0)
if param_test.status:
raise Exception
except Exception as err:
param_test.status = 1
param_test.output += str(err)
return update_param(param_test)
param_test.output += o
param_test.results['duration'] = time.time() - time_start
# test integrity
if param_test.test_integrity:
param_test.output += '\n\n====================================================================================================\n' + 'INTEGRITY TESTING' + '\n====================================================================================================\n\n' # copy command
try:
os.chdir(param_test.path_output)
param_test = module_testing.test_integrity(param_test)
os.chdir(path_testing)
except Exception as err:
os.chdir(path_testing)
param_test.status = 2
param_test.output += str(err)
return update_param(param_test)
return update_param(param_test)
def main(args=None):
# initializations
initz = ''
initcenter = ''
fname_initlabel = ''
file_labelz = 'labelz.nii.gz'
param = Param()
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(args)
fname_in = os.path.abspath(arguments["-i"])
fname_seg = os.path.abspath(arguments['-s'])
contrast = arguments['-c']
path_template = arguments['-t']
scale_dist = arguments['-scale-dist']
if '-ofolder' in arguments:
path_output = arguments['-ofolder']
else:
path_output = os.curdir
param.path_qc = arguments.get("-qc", None)
if '-discfile' in arguments:
fname_disc = os.path.abspath(arguments['-discfile'])
else:
fname_disc = None
if '-initz' in arguments:
initz = arguments['-initz']
if '-initcenter' in arguments:
initcenter = arguments['-initcenter']
# if user provided text file, parse and overwrite arguments
if '-initfile' in arguments:
file = open(arguments['-initfile'], 'r')
initfile = ' ' + file.read().replace('\n', '')
arg_initfile = initfile.split(' ')
for idx_arg, arg in enumerate(arg_initfile):
if arg == '-initz':
initz = [int(x) for x in arg_initfile[idx_arg + 1].split(',')]
if arg == '-initcenter':
initcenter = int(arg_initfile[idx_arg + 1])
if '-initlabel' in arguments:
# get absolute path of label
fname_initlabel = os.path.abspath(arguments['-initlabel'])
if '-param' in arguments:
param.update(arguments['-param'][0])
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
remove_temp_files = int(arguments['-r'])
denoise = int(arguments['-denoise'])
laplacian = int(arguments['-laplacian'])
path_tmp = sct.tmp_create(basename="label_vertebrae", verbose=verbose)
# Copying input data to tmp folder
sct.printv('\nCopying input data to tmp folder...', verbose)
Image(fname_in).save(os.path.join(path_tmp, "data.nii"))
Image(fname_seg).save(os.path.join(path_tmp, "segmentation.nii"))
# Go go temp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Straighten spinal cord
sct.printv('\nStraighten spinal cord...', verbose)
# check if warp_curve2straight and warp_straight2curve already exist (i.e. no need to do it another time)
cache_sig = sct.cache_signature(
input_files=[fname_in, fname_seg],
)
cachefile = os.path.join(curdir, "straightening.cache")
if sct.cache_valid(cachefile, cache_sig) and os.path.isfile(os.path.join(curdir, "warp_curve2straight.nii.gz")) and os.path.isfile(os.path.join(curdir, "warp_straight2curve.nii.gz")) and os.path.isfile(os.path.join(curdir, "straight_ref.nii.gz")):
# if they exist, copy them into current folder
sct.printv('Reusing existing warping field which seems to be valid', verbose, 'warning')
sct.copy(os.path.join(curdir, "warp_curve2straight.nii.gz"), 'warp_curve2straight.nii.gz')
sct.copy(os.path.join(curdir, "warp_straight2curve.nii.gz"), 'warp_straight2curve.nii.gz')
sct.copy(os.path.join(curdir, "straight_ref.nii.gz"), 'straight_ref.nii.gz')
# apply straightening
s, o = sct.run(['sct_apply_transfo', '-i', 'data.nii', '-w', 'warp_curve2straight.nii.gz', '-d', 'straight_ref.nii.gz', '-o', 'data_straight.nii'])
else:
cmd = ['sct_straighten_spinalcord',
'-i', 'data.nii',
'-s', 'segmentation.nii',
'-r', str(remove_temp_files)]
if param.path_qc is not None and os.environ.get("SCT_RECURSIVE_QC", None) == "1":
cmd += ['-qc', param.path_qc]
s, o = sct.run(cmd)
sct.cache_save(cachefile, cache_sig)
# resample to 0.5mm isotropic to match template resolution
sct.printv('\nResample to 0.5mm isotropic...', verbose)
s, o = sct.run(['sct_resample', '-i', 'data_straight.nii', '-mm', '0.5x0.5x0.5', '-x', 'linear', '-o', 'data_straightr.nii'], verbose=verbose)
# Apply straightening to segmentation
# N.B. Output is RPI
sct.printv('\nApply straightening to segmentation...', verbose)
sct.run('isct_antsApplyTransforms -d 3 -i %s -r %s -t %s -o %s -n %s' %
('segmentation.nii',
'data_straightr.nii',
'warp_curve2straight.nii.gz',
'segmentation_straight.nii',
'Linear'),
verbose=verbose,
is_sct_binary=True,
)
# Threshold segmentation at 0.5
sct.run(['sct_maths', '-i', 'segmentation_straight.nii', '-thr', '0.5', '-o', 'segmentation_straight.nii'], verbose)
# If disc label file is provided, label vertebrae using that file instead of automatically
if fname_disc:
# Apply straightening to disc-label
sct.printv('\nApply straightening to disc labels...', verbose)
sct.run('isct_antsApplyTransforms -d 3 -i %s -r %s -t %s -o %s -n %s' %
(fname_disc,
'data_straightr.nii',
'warp_curve2straight.nii.gz',
'labeldisc_straight.nii.gz',
'NearestNeighbor'),
verbose=verbose,
is_sct_binary=True,
)
label_vert('segmentation_straight.nii', 'labeldisc_straight.nii.gz', verbose=1)
else:
# create label to identify disc
sct.printv('\nCreate label to identify disc...', verbose)
fname_labelz = os.path.join(path_tmp, file_labelz)
if initz or initcenter:
if initcenter:
# find z centered in FOV
nii = Image('segmentation.nii').change_orientation("RPI")
nx, ny, nz, nt, px, py, pz, pt = nii.dim # Get dimensions
z_center = int(np.round(nz / 2)) # get z_center
initz = [z_center, initcenter]
# create single label and output as labels.nii.gz
label = ProcessLabels('segmentation.nii', fname_output='tmp.labelz.nii.gz',
coordinates=['{},{}'.format(initz[0], initz[1])])
im_label = label.process('create-seg')
im_label.data = sct_maths.dilate(im_label.data, [3]) # TODO: create a dilation method specific to labels,
# which does not apply a convolution across all voxels (highly inneficient)
im_label.save(fname_labelz)
elif fname_initlabel:
import sct_label_utils
# subtract "1" to label value because due to legacy, in this code the disc C2-C3 has value "2", whereas in the
# recent version of SCT it is defined as "3". Therefore, when asking the user to define a label, we point to the
# new definition of labels (i.e., C2-C3 = 3).
sct_label_utils.main(['-i', fname_initlabel, '-add', '-1', '-o', fname_labelz])
else:
# automatically finds C2-C3 disc
im_data = Image('data.nii')
im_seg = Image('segmentation.nii')
im_label_c2c3 = detect_c2c3(im_data, im_seg, contrast)
ind_label = np.where(im_label_c2c3.data)
if not np.size(ind_label) == 0:
# subtract "1" to label value because due to legacy, in this code the disc C2-C3 has value "2", whereas in the
# recent version of SCT it is defined as "3".
im_label_c2c3.data[ind_label] = 2
else:
sct.printv('Automatic C2-C3 detection failed. Please provide manual label with sct_label_utils', 1, 'error')
im_label_c2c3.save(fname_labelz)
# dilate label so it is not lost when applying warping
sct_maths.main(['-i', fname_labelz, '-dilate', '3', '-o', fname_labelz])
# Apply straightening to z-label
sct.printv('\nAnd apply straightening to label...', verbose)
sct.run('isct_antsApplyTransforms -d 3 -i %s -r %s -t %s -o %s -n %s' %
(file_labelz,
'data_straightr.nii',
'warp_curve2straight.nii.gz',
'labelz_straight.nii.gz',
'NearestNeighbor'),
verbose=verbose,
is_sct_binary=True,
)
# get z value and disk value to initialize labeling
sct.printv('\nGet z and disc values from straight label...', verbose)
init_disc = get_z_and_disc_values_from_label('labelz_straight.nii.gz')
sct.printv('.. ' + str(init_disc), verbose)
# denoise data
if denoise:
sct.printv('\nDenoise data...', verbose)
sct.run(['sct_maths', '-i', 'data_straightr.nii', '-denoise', 'h=0.05', '-o', 'data_straightr.nii'], verbose)
# apply laplacian filtering
if laplacian:
sct.printv('\nApply Laplacian filter...', verbose)
sct.run(['sct_maths', '-i', 'data_straightr.nii', '-laplacian', '1', '-o', 'data_straightr.nii'], verbose)
# detect vertebral levels on straight spinal cord
vertebral_detection('data_straightr.nii', 'segmentation_straight.nii', contrast, param, init_disc=init_disc,
verbose=verbose, path_template=path_template, path_output=path_output, scale_dist=scale_dist)
# un-straighten labeled spinal cord
sct.printv('\nUn-straighten labeling...', verbose)
sct.run('isct_antsApplyTransforms -d 3 -i %s -r %s -t %s -o %s -n %s' %
('segmentation_straight_labeled.nii',
'segmentation.nii',
'warp_straight2curve.nii.gz',
'segmentation_labeled.nii',
'NearestNeighbor'),
verbose=verbose,
is_sct_binary=True,
)
# Clean labeled segmentation
sct.printv('\nClean labeled segmentation (correct interpolation errors)...', verbose)
clean_labeled_segmentation('segmentation_labeled.nii', 'segmentation.nii', 'segmentation_labeled.nii')
# label discs
sct.printv('\nLabel discs...', verbose)
label_discs('segmentation_labeled.nii', verbose=verbose)
# come back
os.chdir(curdir)
# Generate output files
path_seg, file_seg, ext_seg = sct.extract_fname(fname_seg)
fname_seg_labeled = os.path.join(path_output, file_seg + '_labeled' + ext_seg)
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(os.path.join(path_tmp, "segmentation_labeled.nii"), fname_seg_labeled)
sct.generate_output_file(os.path.join(path_tmp, "segmentation_labeled_disc.nii"), os.path.join(path_output, file_seg + '_labeled_discs' + ext_seg))
# copy straightening files in case subsequent SCT functions need them
sct.generate_output_file(os.path.join(path_tmp, "warp_curve2straight.nii.gz"), os.path.join(path_output, "warp_curve2straight.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "warp_straight2curve.nii.gz"), os.path.join(path_output, "warp_straight2curve.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "straight_ref.nii.gz"), os.path.join(path_output, "straight_ref.nii.gz"), verbose)
# Remove temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...', verbose)
sct.rmtree(path_tmp)
# Generate QC report
if param.path_qc is not None:
path_qc = os.path.abspath(param.path_qc)
qc_dataset = arguments.get("-qc-dataset", None)
qc_subject = arguments.get("-qc-subject", None)
labeled_seg_file = os.path.join(path_output, file_seg + '_labeled' + ext_seg)
generate_qc(fname_in, fname_seg=labeled_seg_file, args=args, path_qc=os.path.abspath(path_qc),
dataset=qc_dataset, subject=qc_subject, process='sct_label_vertebrae')
sct.display_viewer_syntax([fname_in, fname_seg_labeled], colormaps=['', 'subcortical'], opacities=['1', '0.5'])
def main(args=None):
if args is None:
args = sys.argv[1:]
# get parser
parser = get_parser()
arguments = parser.parse(args)
if '-d' in arguments:
param.download = int(arguments['-d'])
if '-p' in arguments:
param.path_data = arguments['-p']
if '-f' in arguments:
param.function_to_test = arguments['-f']
if '-r' in arguments:
param.remove_tmp_file = int(arguments['-r'])
# path_data = param.path_data
function_to_test = param.function_to_test
start_time = time.time()
# get absolute path and add slash at the end
param.path_data = sct.slash_at_the_end(os.path.abspath(param.path_data), 1)
# check existence of testing data folder
if not os.path.isdir(param.path_data) or param.download:
downloaddata()
# display path to data
sct.printv('\nPath to testing data: ' + param.path_data, param.verbose)
# create temp folder that will have all results and go in it
param.path_tmp = sct.tmp_create()
os.chdir(param.path_tmp)
# get list of all scripts to test
functions = fill_functions()
if function_to_test:
if not function_to_test in functions:
sct.printv('Function "%s" is not part of the list of testing functions' % function_to_test, type='warning')
# loop across all functions and test them
status = [test_function(f) for f in functions if function_to_test == f]
else:
status = [test_function(f) for f in functions]
sct.printv('status: ' + str(status))
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('Finished! Elapsed time: ' + str(int(round(elapsed_time))) + 's\n')
# remove temp files
if param.remove_tmp_file:
sct.printv('\nRemove temporary files...', param.verbose)
sct.run('rm -rf ' + param.path_tmp, param.verbose)
e = 0
if sum(status) != 0:
e = 1
sct.printv(e)
sys.exit(e)
def main():
# Initialization
fname_data = ''
interp_factor = param.interp_factor
remove_temp_files = param.remove_temp_files
verbose = param.verbose
suffix = param.suffix
smoothing_sigma = param.smoothing_sigma
# start timer
start_time = time.time()
# Parameters for debug mode
if param.debug:
fname_data = os.path.join(sct.__data_dir__, 'sct_testing_data', 't2', 't2_seg.nii.gz')
remove_temp_files = 0
param.mask_size = 10
else:
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'hi:v:r:s:')
except getopt.GetoptError:
usage()
if not opts:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-i'):
fname_data = arg
elif opt in ('-r'):
remove_temp_files = int(arg)
elif opt in ('-s'):
smoothing_sigma = arg
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname_data == '':
usage()
# sct.printv(arguments)
sct.printv('\nCheck parameters:')
sct.printv(' segmentation ........... ' + fname_data)
sct.printv(' interp factor .......... ' + str(interp_factor))
sct.printv(' smoothing sigma ........ ' + str(smoothing_sigma))
# check existence of input files
sct.printv('\nCheck existence of input files...')
sct.check_file_exist(fname_data, verbose)
# Extract path, file and extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
path_tmp = sct.tmp_create(basename="binary_to_trilinear", verbose=verbose)
from sct_convert import convert
sct.printv('\nCopying input data to tmp folder and convert to nii...', param.verbose)
convert(fname_data, os.path.join(path_tmp, "data.nii"))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image('data.nii').dim
sct.printv('.. ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), verbose)
# upsample data
sct.printv('\nUpsample data...', verbose)
sct.run(["sct_resample",
"-i", "data.nii",
"-x", "linear",
"-vox", str(nx * interp_factor) + 'x' + str(ny * interp_factor) + 'x' + str(nz * interp_factor),
"-o", "data_up.nii"], verbose)
# Smooth along centerline
sct.printv('\nSmooth along centerline...', verbose)
sct.run(["sct_smooth_spinalcord",
"-i", "data_up.nii",
"-s", "data_up.nii",
"-smooth", str(smoothing_sigma),
"-r", str(remove_temp_files),
"-v", str(verbose)], verbose)
# downsample data
sct.printv('\nDownsample data...', verbose)
sct.run(["sct_resample",
"-i", "data_up_smooth.nii",
"-x", "linear",
"-vox", str(nx) + 'x' + str(ny) + 'x' + str(nz),
"-o", "data_up_smooth_down.nii"], verbose)
# come back
os.chdir(curdir)
# Generate output files
sct.printv('\nGenerate output files...')
fname_out = sct.generate_output_file(os.path.join(path_tmp, "data_up_smooth_down.nii"), '' + file_data + suffix + ext_data)
# Delete temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...')
sct.rmtree(path_tmp)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's')
# to view results
sct.printv('\nTo view results, type:')
sct.printv('fslview ' + file_data + ' ' + file_data + suffix + ' &\n')
def merge_images(list_fname_src, fname_dest, list_fname_warp, param):
"""
Merge multiple source images onto destination space. All images are warped to the destination space and then added.
To deal with overlap during merging (e.g. one voxel in destination image is shared with two input images), the
resulting voxel is divided by the sum of the partial volume of each image. For example, if src(x,y,z)=1 is mapped to
dest(i,j,k) with a partial volume of 0.5 (because destination voxel is bigger), then its value after linear interpolation
will be 0.5. To account for partial volume, the resulting voxel will be: dest(i,j,k) = 0.5*0.5/0.5 = 0.5.
Now, if two voxels overlap in the destination space, let's say: src(x,y,z)=1 and src2'(x',y',z')=1, then the
resulting value will be: dest(i,j,k) = (0.5*0.5 + 0.5*0.5) / (0.5+0.5) = 0.5. So this function acts like a weighted
average operator, only in destination voxels that share multiple source voxels.
Parameters
----------
list_fname_src
fname_dest
list_fname_warp
param
Returns
-------
"""
# create temporary folder
path_tmp = sct.tmp_create()
# get dimensions of destination file
nii_dest = msct_image.Image(fname_dest)
# initialize variables
data = np.zeros([
nii_dest.dim[0], nii_dest.dim[1], nii_dest.dim[2],
len(list_fname_src)
])
partial_volume = np.zeros([
nii_dest.dim[0], nii_dest.dim[1], nii_dest.dim[2],
len(list_fname_src)
])
data_merge = np.zeros([nii_dest.dim[0], nii_dest.dim[1], nii_dest.dim[2]])
# loop across files
i_file = 0
for fname_src in list_fname_src:
# apply transformation src --> dest
sct_apply_transfo.main(args=[
'-i', fname_src, '-d', fname_dest, '-w', list_fname_warp[i_file],
'-x', param.interp, '-o', 'src_' + str(i_file) +
'_template.nii.gz', '-v', param.verbose
])
# create binary mask from input file by assigning one to all non-null voxels
sct_maths.main(args=[
'-i', fname_src, '-bin',
str(param.almost_zero), '-o', 'src_' + str(i_file) +
'native_bin.nii.gz'
])
# apply transformation to binary mask to compute partial volume
sct_apply_transfo.main(args=[
'-i', 'src_' + str(i_file) + 'native_bin.nii.gz', '-d', fname_dest,
'-w', list_fname_warp[i_file], '-x', param.interp, '-o', 'src_' +
str(i_file) + '_template_partialVolume.nii.gz'
])
# open data
data[:, :, :, i_file] = msct_image.Image('src_' + str(i_file) +
'_template.nii.gz').data
partial_volume[:, :, :, i_file] = msct_image.Image(
'src_' + str(i_file) + '_template_partialVolume.nii.gz').data
i_file += 1
# merge files using partial volume information (and convert nan resulting from division by zero to zeros)
data_merge = np.divide(np.sum(data * partial_volume, axis=3),
np.sum(partial_volume, axis=3))
data_merge = np.nan_to_num(data_merge)
# write result in file
nii_dest.data = data_merge
nii_dest.setFileName(param.fname_out)
nii_dest.save()
# remove temporary folder
if param.rm_tmp:
sct.rmtree(path_tmp)
def main(args=None):
if not args:
args = sys.argv[1:]
# initialize parameters
param = Param()
# call main function
parser = get_parser()
arguments = parser.parse(args)
fname_data = arguments['-i']
fname_bvecs = arguments['-bvec']
average = arguments['-a']
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
remove_temp_files = int(arguments['-r'])
path_out = arguments['-ofolder']
if '-bval' in arguments:
fname_bvals = arguments['-bval']
else:
fname_bvals = ''
if '-bvalmin' in arguments:
param.bval_min = arguments['-bvalmin']
# Initialization
start_time = time.time()
# sct.printv(arguments)
sct.printv('\nInput parameters:', verbose)
sct.printv(' input file ............' + fname_data, verbose)
sct.printv(' bvecs file ............' + fname_bvecs, verbose)
sct.printv(' bvals file ............' + fname_bvals, verbose)
sct.printv(' average ...............' + str(average), verbose)
# Get full path
fname_data = os.path.abspath(fname_data)
fname_bvecs = os.path.abspath(fname_bvecs)
if fname_bvals:
fname_bvals = os.path.abspath(fname_bvals)
# Extract path, file and extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
# create temporary folder
path_tmp = sct.tmp_create(basename="dmri_separate", verbose=verbose)
# copy files into tmp folder and convert to nifti
sct.printv('\nCopy files into temporary folder...', verbose)
ext = '.nii'
dmri_name = 'dmri'
b0_name = file_data + '_b0'
b0_mean_name = b0_name + '_mean'
dwi_name = file_data + '_dwi'
dwi_mean_name = dwi_name + '_mean'
if not convert(fname_data, os.path.join(path_tmp, dmri_name + ext)):
sct.printv('ERROR in convert.', 1, 'error')
sct.copy(fname_bvecs, os.path.join(path_tmp, "bvecs"), verbose=verbose)
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Get size of data
im_dmri = Image(dmri_name + ext)
sct.printv('\nGet dimensions data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = im_dmri.dim
sct.printv('.. ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt), verbose)
# Identify b=0 and DWI images
sct.printv(fname_bvals)
index_b0, index_dwi, nb_b0, nb_dwi = identify_b0(fname_bvecs, fname_bvals, param.bval_min, verbose)
# Split into T dimension
sct.printv('\nSplit along T dimension...', verbose)
im_dmri_split_list = split_data(im_dmri, 3)
for im_d in im_dmri_split_list:
im_d.save()
# Merge b=0 images
sct.printv('\nMerge b=0...', verbose)
from sct_image import concat_data
l = []
for it in range(nb_b0):
l.append(dmri_name + '_T' + str(index_b0[it]).zfill(4) + ext)
im_out = concat_data(l, 3).save(b0_name + ext)
# Average b=0 images
if average:
sct.printv('\nAverage b=0...', verbose)
sct.run(['sct_maths', '-i', b0_name + ext, '-o', b0_mean_name + ext, '-mean', 't'], verbose)
# Merge DWI
l = []
for it in range(nb_dwi):
l.append(dmri_name + '_T' + str(index_dwi[it]).zfill(4) + ext)
im_out = concat_data(l, 3).save(dwi_name + ext)
# Average DWI images
if average:
sct.printv('\nAverage DWI...', verbose)
sct.run(['sct_maths', '-i', dwi_name + ext, '-o', dwi_mean_name + ext, '-mean', 't'], verbose)
# come back
os.chdir(curdir)
# Generate output files
fname_b0 = os.path.abspath(os.path.join(path_out, b0_name + ext_data))
fname_dwi = os.path.abspath(os.path.join(path_out, dwi_name + ext_data))
fname_b0_mean = os.path.abspath(os.path.join(path_out, b0_mean_name + ext_data))
fname_dwi_mean = os.path.abspath(os.path.join(path_out, dwi_mean_name + ext_data))
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(os.path.join(path_tmp, b0_name + ext), fname_b0, verbose)
sct.generate_output_file(os.path.join(path_tmp, dwi_name + ext), fname_dwi, verbose)
if average:
sct.generate_output_file(os.path.join(path_tmp, b0_mean_name + ext), fname_b0_mean, verbose)
sct.generate_output_file(os.path.join(path_tmp, dwi_mean_name + ext), fname_dwi_mean, verbose)
# Remove temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's', verbose)
return fname_b0, fname_b0_mean, fname_dwi, fname_dwi_mean
input_second_fname = ''
output_fname = 'hausdorff_distance.txt'
resample_to = 0.1
if "-d" in arguments:
input_second_fname = arguments["-d"]
if "-thinning" in arguments:
param.thinning = bool(int(arguments["-thinning"]))
if "-resampling" in arguments:
resample_to = arguments["-resampling"]
if "-o" in arguments:
output_fname = arguments["-o"]
param.verbose = int(arguments.get('-v'))
sct.init_sct(log_level=param.verbose, update=True) # Update log level
tmp_dir = sct.tmp_create()
im1_name = "im1.nii.gz"
sct.copy(input_fname, os.path.join(tmp_dir, im1_name))
if input_second_fname != '':
im2_name = 'im2.nii.gz'
sct.copy(input_second_fname, os.path.join(tmp_dir, im2_name))
else:
im2_name = None
curdir = os.getcwd()
os.chdir(tmp_dir)
# now = time.time()
input_im1 = Image(
resample_image(im1_name,
binary=True,
def __init__(self, fname_mask, fname_sc, fname_ref, path_template,
path_ofolder, verbose):
self.fname_mask = fname_mask
self.fname_sc = fname_sc
self.fname_ref = fname_ref
self.path_template = path_template
self.path_ofolder = path_ofolder
self.verbose = verbose
self.wrk_dir = os.getcwd()
if not set(np.unique(Image(fname_mask).data)) == set([0.0, 1.0]):
if set(np.unique(Image(fname_mask).data)) == set([0.0]):
printv('WARNING: Empty masked image', self.verbose, 'warning')
else:
printv(
"ERROR input file %s is not binary file with 0 and 1 values"
% fname_mask, 1, 'error')
# create tmp directory
self.tmp_dir = tmp_create(verbose=verbose) # path to tmp directory
# lesion file where each lesion has a different value
self.fname_label = extract_fname(
self.fname_mask)[1] + '_label' + extract_fname(self.fname_mask)[2]
# initialization of measure sheet
measure_lst = [
'label', 'volume [mm3]', 'length [mm]',
'max_equivalent_diameter [mm]'
]
if self.fname_ref is not None:
for measure in ['mean', 'std']:
measure_lst.append(measure + '_' +
extract_fname(self.fname_ref)[1])
measure_dct = {}
for column in measure_lst:
measure_dct[column] = None
self.measure_pd = pd.DataFrame(data=measure_dct,
index=range(0),
columns=measure_lst)
# orientation of the input image
self.orientation = None
# volume object
self.volumes = None
# initialization of proportion measures, related to registrated atlas
if self.path_template is not None:
self.path_atlas = self.path_template + 'atlas/'
self.path_levels = self.path_template + 'template/PAM50_levels.nii.gz'
else:
self.path_atlas, self.path_levels = None, None
self.vert_lst = None
self.atlas_roi_lst = None
self.distrib_matrix_dct = {}
# output names
self.pickle_name = extract_fname(self.fname_mask)[1] + '_analyzis.pkl'
self.excel_name = extract_fname(self.fname_mask)[1] + '_analyzis.xls'
def main(args=None):
if args is None:
args = sys.argv[1:]
# initialize parameters
param = Param()
# Initialization
fname_output = ''
path_out = ''
fname_src_seg = ''
fname_dest_seg = ''
fname_src_label = ''
fname_dest_label = ''
generate_warpinv = 1
start_time = time.time()
# get path of the toolbox
status, path_sct = commands.getstatusoutput('echo $SCT_DIR')
# get default registration parameters
# step1 = Paramreg(step='1', type='im', algo='syn', metric='MI', iter='5', shrink='1', smooth='0', gradStep='0.5')
step0 = Paramreg(step='0', type='im', algo='syn', metric='MI', iter='0', shrink='1', smooth='0', gradStep='0.5', slicewise='0', dof='Tx_Ty_Tz_Rx_Ry_Rz') # only used to put src into dest space
step1 = Paramreg(step='1', type='im')
paramreg = ParamregMultiStep([step0, step1])
parser = get_parser(paramreg=paramreg)
arguments = parser.parse(args)
# get arguments
fname_src = arguments['-i']
fname_dest = arguments['-d']
if '-iseg' in arguments:
fname_src_seg = arguments['-iseg']
if '-dseg' in arguments:
fname_dest_seg = arguments['-dseg']
if '-ilabel' in arguments:
fname_src_label = arguments['-ilabel']
if '-dlabel' in arguments:
fname_dest_label = arguments['-dlabel']
if '-o' in arguments:
fname_output = arguments['-o']
if '-ofolder' in arguments:
path_out = arguments['-ofolder']
if '-owarp' in arguments:
fname_output_warp = arguments['-owarp']
else:
fname_output_warp = ''
if '-initwarp' in arguments:
fname_initwarp = os.path.abspath(arguments['-initwarp'])
else:
fname_initwarp = ''
if '-initwarpinv' in arguments:
fname_initwarpinv = os.path.abspath(arguments['-initwarpinv'])
else:
fname_initwarpinv = ''
if '-m' in arguments:
fname_mask = arguments['-m']
else:
fname_mask = ''
padding = arguments['-z']
if "-param" in arguments:
paramreg_user = arguments['-param']
# update registration parameters
for paramStep in paramreg_user:
paramreg.addStep(paramStep)
identity = int(arguments['-identity'])
interp = arguments['-x']
remove_temp_files = int(arguments['-r'])
verbose = int(arguments['-v'])
# print arguments
print '\nInput parameters:'
print ' Source .............. '+fname_src
print ' Destination ......... '+fname_dest
print ' Init transfo ........ '+fname_initwarp
print ' Mask ................ '+fname_mask
print ' Output name ......... '+fname_output
# print ' Algorithm ........... '+paramreg.algo
# print ' Number of iterations '+paramreg.iter
# print ' Metric .............. '+paramreg.metric
print ' Remove temp files ... '+str(remove_temp_files)
print ' Verbose ............. '+str(verbose)
# update param
param.verbose = verbose
param.padding = padding
param.fname_mask = fname_mask
param.remove_temp_files = remove_temp_files
# Get if input is 3D
sct.printv('\nCheck if input data are 3D...', verbose)
sct.check_if_3d(fname_src)
sct.check_if_3d(fname_dest)
# Check if user selected type=seg, but did not input segmentation data
if 'paramreg_user' in locals():
if True in ['type=seg' in paramreg_user[i] for i in range(len(paramreg_user))]:
if fname_src_seg == '' or fname_dest_seg == '':
sct.printv('\nERROR: if you select type=seg you must specify -iseg and -dseg flags.\n', 1, 'error')
# Extract path, file and extension
path_src, file_src, ext_src = sct.extract_fname(fname_src)
path_dest, file_dest, ext_dest = sct.extract_fname(fname_dest)
# check if source and destination images have the same name (related to issue #373)
# If so, change names to avoid conflict of result files and warns the user
suffix_src, suffix_dest = '_reg', '_reg'
if file_src == file_dest:
suffix_src, suffix_dest = '_src_reg', '_dest_reg'
# define output folder and file name
if fname_output == '':
path_out = '' if not path_out else path_out # output in user's current directory
file_out = file_src + suffix_src
file_out_inv = file_dest + suffix_dest
ext_out = ext_src
else:
path, file_out, ext_out = sct.extract_fname(fname_output)
path_out = path if not path_out else path_out
file_out_inv = file_out + '_inv'
# create QC folder
sct.create_folder(param.path_qc)
# create temporary folder
path_tmp = sct.tmp_create()
# copy files to temporary folder
from sct_convert import convert
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
convert(fname_src, path_tmp+'src.nii')
convert(fname_dest, path_tmp+'dest.nii')
if fname_src_seg:
convert(fname_src_seg, path_tmp+'src_seg.nii')
convert(fname_dest_seg, path_tmp+'dest_seg.nii')
if fname_src_label:
convert(fname_src_label, path_tmp+'src_label.nii')
convert(fname_dest_label, path_tmp+'dest_label.nii')
if fname_mask != '':
convert(fname_mask, path_tmp+'mask.nii.gz')
# go to tmp folder
os.chdir(path_tmp)
# reorient destination to RPI
sct.run('sct_image -i dest.nii -setorient RPI -o dest_RPI.nii')
if fname_dest_seg:
sct.run('sct_image -i dest_seg.nii -setorient RPI -o dest_seg_RPI.nii')
if fname_dest_label:
sct.run('sct_image -i dest_label.nii -setorient RPI -o dest_label_RPI.nii')
if identity:
# overwrite paramreg and only do one identity transformation
step0 = Paramreg(step='0', type='im', algo='syn', metric='MI', iter='0', shrink='1', smooth='0', gradStep='0.5')
paramreg = ParamregMultiStep([step0])
# Put source into destination space using header (no estimation -- purely based on header)
# TODO: Check if necessary to do that
# TODO: use that as step=0
# sct.printv('\nPut source into destination space using header...', verbose)
# sct.run('isct_antsRegistration -d 3 -t Translation[0] -m MI[dest_pad.nii,src.nii,1,16] -c 0 -f 1 -s 0 -o [regAffine,src_regAffine.nii] -n BSpline[3]', verbose)
# if segmentation, also do it for seg
# initialize list of warping fields
warp_forward = []
warp_inverse = []
# initial warping is specified, update list of warping fields and skip step=0
if fname_initwarp:
sct.printv('\nSkip step=0 and replace with initial transformations: ', param.verbose)
sct.printv(' '+fname_initwarp, param.verbose)
# sct.run('cp '+fname_initwarp+' warp_forward_0.nii.gz', verbose)
warp_forward = [fname_initwarp]
start_step = 1
if fname_initwarpinv:
warp_inverse = [fname_initwarpinv]
else:
sct.printv('\nWARNING: No initial inverse warping field was specified, therefore the inverse warping field will NOT be generated.', param.verbose, 'warning')
generate_warpinv = 0
else:
start_step = 0
# loop across registration steps
for i_step in range(start_step, len(paramreg.steps)):
sct.printv('\n--\nESTIMATE TRANSFORMATION FOR STEP #'+str(i_step), param.verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = 'src.nii'
dest = 'dest_RPI.nii'
interp_step = 'spline'
elif paramreg.steps[str(i_step)].type == 'seg':
src = 'src_seg.nii'
dest = 'dest_seg_RPI.nii'
interp_step = 'nn'
elif paramreg.steps[str(i_step)].type == 'label':
src = 'src_label.nii'
dest = 'dest_label_RPI.nii'
interp_step = 'nn'
else:
# src = dest = interp_step = None
sct.printv('ERROR: Wrong image type.', 1, 'error')
# if step>0, apply warp_forward_concat to the src image to be used
if i_step > 0:
sct.printv('\nApply transformation from previous step', param.verbose)
sct.run('sct_apply_transfo -i '+src+' -d '+dest+' -w '+','.join(warp_forward)+' -o '+sct.add_suffix(src, '_reg')+' -x '+interp_step, verbose)
src = sct.add_suffix(src, '_reg')
# register src --> dest
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.insert(0, warp_inverse_out)
# Concatenate transformations
sct.printv('\nConcatenate transformations...', verbose)
sct.run('sct_concat_transfo -w '+','.join(warp_forward)+' -d dest.nii -o warp_src2dest.nii.gz', verbose)
sct.run('sct_concat_transfo -w '+','.join(warp_inverse)+' -d dest.nii -o warp_dest2src.nii.gz', verbose)
# Apply warping field to src data
sct.printv('\nApply transfo source --> dest...', verbose)
sct.run('sct_apply_transfo -i src.nii -o src_reg.nii -d dest.nii -w warp_src2dest.nii.gz -x '+interp, verbose)
sct.printv('\nApply transfo dest --> source...', verbose)
sct.run('sct_apply_transfo -i dest.nii -o dest_reg.nii -d src.nii -w warp_dest2src.nii.gz -x '+interp, verbose)
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', verbose)
# generate: src_reg
fname_src2dest = sct.generate_output_file(path_tmp+'src_reg.nii', path_out+file_out+ext_out, verbose)
# generate: forward warping field
if fname_output_warp == '':
fname_output_warp = path_out+'warp_'+file_src+'2'+file_dest+'.nii.gz'
sct.generate_output_file(path_tmp+'warp_src2dest.nii.gz', fname_output_warp, verbose)
if generate_warpinv:
# generate: dest_reg
fname_dest2src = sct.generate_output_file(path_tmp+'dest_reg.nii', path_out+file_out_inv+ext_dest, verbose)
# generate: inverse warping field
sct.generate_output_file(path_tmp+'warp_dest2src.nii.gz', path_out+'warp_'+file_dest+'2'+file_src+'.nii.gz', verbose)
# Delete temporary files
if remove_temp_files:
sct.printv('\nRemove temporary files...', verbose)
sct.run('rm -rf '+path_tmp, verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: '+str(int(round(elapsed_time)))+'s', verbose)
sct.printv('\nTo view results, type:', verbose)
sct.printv('fslview '+fname_dest+' '+fname_src2dest+' &', verbose, 'info')
if generate_warpinv:
sct.printv('fslview '+fname_src+' '+fname_dest2src+' &\n', verbose, 'info')
def main(args=None):
# initializations
initz = ''
initcenter = ''
initc2 = 'auto'
param = Param()
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
fname_in = arguments["-i"]
fname_seg = arguments['-s']
contrast = arguments['-c']
path_template = sct.slash_at_the_end(arguments['-t'], 1)
# if '-o' in arguments:
# file_out = arguments["-o"]
# else:
# file_out = ''
if '-ofolder' in arguments:
path_output = sct.slash_at_the_end(os.path.abspath(arguments['-ofolder']), slash=1)
else:
path_output = sct.slash_at_the_end(os.path.abspath(os.curdir), slash=1)
if '-initz' in arguments:
initz = arguments['-initz']
if '-initcenter' in arguments:
initcenter = arguments['-initcenter']
# if user provided text file, parse and overwrite arguments
if '-initfile' in arguments:
# open file
file = open(arguments['-initfile'], 'r')
initfile = ' '+file.read().replace('\n', '')
arg_initfile = initfile.split(' ')
for i in xrange(len(arg_initfile)):
if arg_initfile[i] == '-initz':
initz = [int(x) for x in arg_initfile[i+1].split(',')]
if arg_initfile[i] == '-initcenter':
initcenter = int(arg_initfile[i+1])
if '-initc2' in arguments:
initc2 = 'manual'
if '-param' in arguments:
param.update(arguments['-param'][0])
verbose = int(arguments['-v'])
remove_tmp_files = int(arguments['-r'])
denoise = int(arguments['-denoise'])
laplacian = int(arguments['-laplacian'])
# if verbose, import matplotlib
# if verbose == 2:
# import matplotlib.pyplot as plt
# create temporary folder
printv('\nCreate temporary folder...', verbose)
path_tmp = tmp_create(verbose=verbose)
# path_tmp = '/Users/julien/Dropbox/documents/processing/20160813_wang/t12/tmp.160814213032_725693/'
# Copying input data to tmp folder
printv('\nCopying input data to tmp folder...', verbose)
run('sct_convert -i '+fname_in+' -o '+path_tmp+'data.nii')
run('sct_convert -i '+fname_seg+' -o '+path_tmp+'segmentation.nii.gz')
# Go go temp folder
os.chdir(path_tmp)
# create label to identify disc
printv('\nCreate label to identify disc...', verbose)
initauto = False
if initz:
create_label_z('segmentation.nii.gz', initz[0], initz[1]) # create label located at z_center
elif initcenter:
# find z centered in FOV
nii = Image('segmentation.nii.gz')
nii.change_orientation('RPI') # reorient to RPI
nx, ny, nz, nt, px, py, pz, pt = nii.dim # Get dimensions
z_center = int(round(nz/2)) # get z_center
create_label_z('segmentation.nii.gz', z_center, initcenter) # create label located at z_center
else:
initauto = True
# printv('\nERROR: You need to initialize the disc detection algorithm using one of these two options: -initz, -initcenter\n', 1, 'error')
# Straighten spinal cord
printv('\nStraighten spinal cord...', 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 data.nii -w warp_curve2straight.nii.gz -d straight_ref.nii.gz -o data_straight.nii')
else:
run('sct_straighten_spinalcord -i data.nii -s segmentation.nii.gz -r 0 -qc 0')
# resample to 0.5mm isotropic to match template resolution
printv('\nResample to 0.5mm isotropic...', verbose)
run('sct_resample -i data_straight.nii -mm 0.5x0.5x0.5 -x linear -o data_straightr.nii', verbose)
# run('sct_resample -i segmentation.nii.gz -mm 0.5x0.5x0.5 -x linear -o segmentationr.nii.gz', verbose)
# run('sct_resample -i labelz.nii.gz -mm 0.5x0.5x0.5 -x linear -o labelzr.nii', verbose)
# Apply straightening to segmentation
# N.B. Output is RPI
printv('\nApply straightening to segmentation...', verbose)
run('sct_apply_transfo -i segmentation.nii.gz -d data_straightr.nii -w warp_curve2straight.nii.gz -o segmentation_straight.nii.gz -x linear', verbose)
# Threshold segmentation at 0.5
run('sct_maths -i segmentation_straight.nii.gz -thr 0.5 -o segmentation_straight.nii.gz', verbose)
if initauto:
init_disc = []
else:
# Apply straightening to z-label
printv('\nDilate z-label and apply straightening...', verbose)
run('sct_apply_transfo -i labelz.nii.gz -d data_straightr.nii -w warp_curve2straight.nii.gz -o labelz_straight.nii.gz -x nn', verbose)
# get z value and disk value to initialize labeling
printv('\nGet z and disc values from straight label...', verbose)
init_disc = get_z_and_disc_values_from_label('labelz_straight.nii.gz')
printv('.. '+str(init_disc), verbose)
# denoise data
if denoise:
printv('\nDenoise data...', verbose)
run('sct_maths -i data_straightr.nii -denoise h=0.05 -o data_straightr.nii', verbose)
# apply laplacian filtering
if laplacian:
printv('\nApply Laplacian filter...', verbose)
run('sct_maths -i data_straightr.nii -laplacian 1 -o data_straightr.nii', verbose)
# detect vertebral levels on straight spinal cord
vertebral_detection('data_straightr.nii', 'segmentation_straight.nii.gz', contrast, param, init_disc=init_disc, verbose=verbose, path_template=path_template, initc2=initc2, path_output=path_output)
# un-straighten labeled spinal cord
printv('\nUn-straighten labeling...', verbose)
run('sct_apply_transfo -i segmentation_straight_labeled.nii.gz -d segmentation.nii.gz -w warp_straight2curve.nii.gz -o segmentation_labeled.nii.gz -x nn', verbose)
# Clean labeled segmentation
printv('\nClean labeled segmentation (correct interpolation errors)...', verbose)
clean_labeled_segmentation('segmentation_labeled.nii.gz', 'segmentation.nii.gz', 'segmentation_labeled.nii.gz')
# label discs
printv('\nLabel discs...', verbose)
label_discs('segmentation_labeled.nii.gz', verbose=verbose)
# come back to parent folder
os.chdir('..')
# Generate output files
path_seg, file_seg, ext_seg = extract_fname(fname_seg)
printv('\nGenerate output files...', verbose)
generate_output_file(path_tmp+'segmentation_labeled.nii.gz', path_output+file_seg+'_labeled'+ext_seg)
generate_output_file(path_tmp+'segmentation_labeled_disc.nii.gz', path_output+file_seg+'_labeled_discs'+ext_seg)
# copy straightening files in case subsequent SCT functions need them
generate_output_file(path_tmp+'warp_curve2straight.nii.gz', path_output+'warp_curve2straight.nii.gz', verbose)
generate_output_file(path_tmp+'warp_straight2curve.nii.gz', path_output+'warp_straight2curve.nii.gz', verbose)
generate_output_file(path_tmp+'straight_ref.nii.gz', path_output+'straight_ref.nii.gz', verbose)
# Remove temporary files
if remove_tmp_files == 1:
printv('\nRemove temporary files...', verbose)
run('rm -rf '+path_tmp)
# to view results
printv('\nDone! To view results, type:', verbose)
printv('fslview '+fname_in+' '+path_output+file_seg+'_labeled'+' -l Random-Rainbow -t 0.5 &\n', verbose, 'info')
def main():
# initialization
fname_mask = ''
# Get parser info
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
fname_data = arguments['-i']
fname_mask = arguments['-m']
vert_label_fname = arguments["-vertfile"]
vert_levels = arguments["-vert"]
slices_of_interest = arguments["-z"]
index_vol = arguments['-vol']
method = arguments["-method"]
remove_temp_files = int(arguments['-r'])
verbose = int(arguments['-v'])
# Check if data are in RPI
input_im = Image(fname_data)
input_orient = get_orientation(input_im)
# If orientation is not RPI, change to RPI
if input_orient != 'RPI':
sct.printv(
'\nCreate temporary folder to change the orientation of the NIFTI files into RPI...',
verbose)
path_tmp = sct.tmp_create()
# change orientation and load data
sct.printv('\nChange input image orientation and load it...', verbose)
input_im_rpi = orientation(input_im,
ori='RPI',
set=True,
fname_out=os.path.join(
path_tmp, "input_RPI.nii"))
input_data = input_im_rpi.data
# Do the same for the mask
sct.printv('\nChange mask orientation and load it...', verbose)
mask_im_rpi = orientation(Image(fname_mask),
ori='RPI',
set=True,
fname_out=os.path.join(
path_tmp, "mask_RPI.nii"))
mask_data = mask_im_rpi.data
# Do the same for vertebral labeling if present
if vert_levels != 'None':
sct.printv(
'\nChange vertebral labeling file orientation and load it...',
verbose)
vert_label_im_rpi = orientation(Image(vert_label_fname),
ori='RPI',
set=True,
fname_out=os.path.join(
path_tmp,
"vert_labeling_RPI.nii"))
vert_labeling_data = vert_label_im_rpi.data
# Remove the temporary folder used to change the NIFTI files orientation into RPI
if remove_temp_files:
sct.printv('\nRemove the temporary folder...', verbose)
sct.rmtree(path_tmp, True)
else:
# Load data
sct.printv('\nLoad data...', verbose)
input_data = input_im.data
mask_data = Image(fname_mask).data
if vert_levels != 'None':
vert_labeling_data = Image(vert_label_fname).data
sct.printv('\tDone.', verbose)
# Get slices corresponding to vertebral levels
if vert_levels != 'None':
from sct_extract_metric import get_slices_matching_with_vertebral_levels
slices_of_interest, actual_vert_levels, warning_vert_levels = get_slices_matching_with_vertebral_levels(
mask_data, vert_levels, vert_labeling_data, verbose)
# Remove slices that were not selected
if slices_of_interest == 'None':
slices_of_interest = '0:' + str(mask_data.shape[2] - 1)
slices_boundary = slices_of_interest.split(':')
slices_of_interest_list = range(int(slices_boundary[0]),
int(slices_boundary[1]) + 1)
# Crop
input_data = input_data[:, :, slices_of_interest_list, :]
mask_data = mask_data[:, :, slices_of_interest_list]
# if user selected all slices (-vol -1), then assign index_vol
if index_vol[0] == -1:
index_vol = range(0, input_data.shape[3], 1)
# Get signal and noise
indexes_roi = np.where(mask_data == 1)
if method == 'mult':
# get voxels in ROI to obtain a (x*y*z)*t 2D matrix
input_data_in_roi = input_data[indexes_roi]
# compute signal and STD across by averaging across time
signal = np.mean(input_data_in_roi[:, index_vol])
std_input_temporal = np.std(input_data_in_roi[:, index_vol], 1)
noise = np.mean(std_input_temporal)
elif method == 'diff':
# if user did not select two volumes, then exit with error
if not len(index_vol) == 2:
sct.printv(
'ERROR: ' + str(len(index_vol)) +
' volumes were specified. Method "diff" should be used with exactly two volumes.',
1, 'error')
data_1 = input_data[:, :, :, index_vol[0]]
data_2 = input_data[:, :, :, index_vol[1]]
# compute voxel-average of voxelwise sum
signal = np.mean(np.add(data_1[indexes_roi], data_2[indexes_roi]))
# compute voxel-STD of voxelwise substraction, multiplied by sqrt(2) as described in equation 7 of Dietrich et al.
noise = np.std(np.subtract(data_1[indexes_roi],
data_2[indexes_roi])) * np.sqrt(2)
# compute SNR
SNR = signal / noise
# Display result
sct.printv('\nSNR_' + method + ' = ' + str(SNR) + '\n', type='info')
def crop_with_gui(self):
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
# Initialization
fname_data = self.input_filename
suffix_out = '_crop'
remove_temp_files = self.rm_tmp_files
verbose = self.verbose
# Check file existence
sct.printv('\nCheck file existence...', verbose)
sct.check_file_exist(fname_data, verbose)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_data).dim
sct.printv('.. ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), verbose)
# check if 4D data
if not nt == 1:
sct.printv('\nERROR in ' + os.path.basename(__file__) + ': Data should be 3D.\n', 1, 'error')
sys.exit(2)
# sct.printv(arguments)
sct.printv('\nCheck parameters:')
sct.printv(' data ................... ' + fname_data)
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
path_out, file_out, ext_out = '', file_data + suffix_out, ext_data
path_tmp = sct.tmp_create() + "/"
# copy files into tmp folder
from sct_convert import convert
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
convert(fname_data, os.path.join(path_tmp, "data.nii"))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# change orientation
sct.printv('\nChange orientation to RPI...', verbose)
Image('data.nii').change_orientation("RPI").save('data_rpi.nii')
# get image of medial slab
sct.printv('\nGet image of medial slab...', verbose)
image_array = nibabel.load('data_rpi.nii').get_data()
nx, ny, nz = image_array.shape
scipy.misc.imsave('image.jpg', image_array[math.floor(nx / 2), :, :])
# Display the image
sct.printv('\nDisplay image and get cropping region...', verbose)
fig = plt.figure()
# fig = plt.gcf()
# ax = plt.gca()
ax = fig.add_subplot(111)
img = mpimg.imread("image.jpg")
implot = ax.imshow(img.T)
implot.set_cmap('gray')
plt.gca().invert_yaxis()
# mouse callback
ax.set_title('Left click on the top and bottom of your cropping field.\n Right click to remove last point.\n Close window when your done.')
line, = ax.plot([], [], 'ro') # empty line
cropping_coordinates = LineBuilder(line)
plt.show()
# disconnect callback
# fig.canvas.mpl_disconnect(line)
# check if user clicked two times
if len(cropping_coordinates.xs) != 2:
sct.printv('\nERROR: You have to select two points. Exit program.\n', 1, 'error')
sys.exit(2)
# convert coordinates to integer
zcrop = [int(i) for i in cropping_coordinates.ys]
# sort coordinates
zcrop.sort()
# crop image
sct.printv('\nCrop image...', verbose)
nii = Image('data_rpi.nii')
data_crop = nii.data[:, :, zcrop[0]:zcrop[1]]
nii.data = data_crop
nii.absolutepath = 'data_rpi_crop.nii'
nii.save()
# come back
os.chdir(curdir)
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(os.path.join(path_tmp, "data_rpi_crop.nii"), os.path.join(path_out, file_out + ext_out))
# Remove temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...')
sct.rmtree(path_tmp)
sct.display_viewer_syntax(files=[os.path.join(path_out, file_out + ext_out)])
def execute(self):
print 'Execution of the SCAD algorithm'
vesselness_file_name = "imageVesselNessFilter.nii.gz"
raw_file_name = "raw.nii"
if self.debug:
import matplotlib.pyplot as plt # import for debug purposes
# create tmp and copy input
path_tmp = sct.tmp_create()
sct.tmp_copy_nifti(self.input_image.absolutepath, path_tmp, raw_file_name)
if self.vesselness_provided:
sct.run('cp '+vesselness_file_name+' '+path_tmp+vesselness_file_name)
os.chdir(path_tmp)
# get input image information
img = Image(raw_file_name)
# save original orientation and change image to RPI
self.raw_orientation = img.change_orientation()
# get body symmetry
sym = SymmetryDetector(raw_file_name, self.contrast, crop_xy=1)
self.raw_symmetry = sym.execute()
# vesselness filter
if not self.vesselness_provided:
sct.run('sct_vesselness -i '+raw_file_name+' -t ' + self._contrast)
# load vesselness filter data and perform minimum path on it
img = Image(vesselness_file_name)
raw_orientation = img.change_orientation()
self.minimum_path_data, self.J1_min_path, self.J2_min_path = get_minimum_path(img.data, invert=1, debug=1, smooth_factor=1)
# Apply an exponent to the minimum path
self.minimum_path_powered = np.power(self.minimum_path_data, self.minimum_path_exponent)
# Saving in Image since smooth_minimal_path needs pixel dimensions
img.data = self.minimum_path_powered
# smooth resulting minimal path
self.smoothed_min_path = smooth_minimal_path(img)
# normalise symmetry values between 0 and 1
normalised_symmetry = equalize_array_histogram(self.raw_symmetry)
# multiply normalised symmetry data with the minimum path result
self.spine_detect_data = np.multiply(self.smoothed_min_path.data, normalised_symmetry)
# extract the centerline from the minimal path image
centerline_with_outliers = get_centerline(self.spine_detect_data, self.spine_detect_data.shape)
img.data = centerline_with_outliers
img.change_orientation()
img.file_name = "centerline_with_outliers"
img.save()
# use a b-spline to smooth out the centerline
x, y, z, dx, dy, dz = smooth_centerline("centerline_with_outliers.nii.gz")
# save the centerline
centerline_dim = img.dim
img.data = np.zeros(centerline_dim)
for i in range(0, np.size(x)-1):
img.data[int(x[i]), int(y[i]), int(z[i])] = 1
img.change_orientation(raw_orientation)
img.file_name = "centerline"
img.save()
# copy back centerline
os.chdir('../')
sct.tmp_copy_nifti(path_tmp + 'centerline.nii.gz',self.input_image.path,self.input_image.file_name+'_centerline'+self.input_image.ext)
if self.rm_tmp_file == 1:
import shutil
shutil.rmtree(path_tmp)
if self.produce_output:
self.produce_output_files()
def main():
# Initialization
size_data = 61
size_label = 1 # put zero for labels that are single points.
dice_acceptable = 0.39 # computed DICE should be 0.931034
test_passed = 0
remove_temp_files = 1
verbose = 1
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'hvr:')
except getopt.GetoptError:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-v'):
verbose = int(arg)
elif opt in ('-r'):
remove_temp_files = int(arg)
path_tmp = sct.tmp_create(basename="test_ants", verbose=verbose)
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Initialise numpy volumes
data_src = np.zeros((size_data, size_data, size_data), dtype=np.int16)
data_dest = np.zeros((size_data, size_data, size_data), dtype=np.int16)
# add labels for src image (curved).
# Labels can be big (more than single point), because when applying NN interpolation, single points might disappear
data_src[20 - size_label:20 + size_label + 1, 20 - size_label:20 + size_label + 1, 10 - size_label:10 + size_label + 1] = 1
data_src[30 - size_label:30 + size_label + 1, 30 - size_label:30 + size_label + 1, 30 - size_label:30 + size_label + 1] = 2
data_src[20 - size_label:20 + size_label + 1, 20 - size_label:20 + size_label + 1, 50 - size_label:50 + size_label + 1] = 3
# add labels for dest image (straight).
# Here, no need for big labels (bigger than single point) because these labels will not be re-interpolated.
data_dest[30 - size_label:30 + size_label + 1, 30 - size_label:30 + size_label + 1, 10 - size_label:10 + size_label + 1] = 1
data_dest[30 - size_label:30 + size_label + 1, 30 - size_label:30 + size_label + 1, 30 - size_label:30 + size_label + 1] = 2
data_dest[30 - size_label:30 + size_label + 1, 30 - size_label:30 + size_label + 1, 50 - size_label:50 + size_label + 1] = 3
# save as nifti
img_src = nib.Nifti1Image(data_src, np.eye(4))
nib.save(img_src, 'data_src.nii.gz')
img_dest = nib.Nifti1Image(data_dest, np.eye(4))
nib.save(img_dest, 'data_dest.nii.gz')
# Estimate rigid transformation
sct.printv('\nEstimate rigid transformation between paired landmarks...', verbose)
# TODO fixup isct_ants* parsers
sct.run(['isct_antsRegistration',
'-d', '3',
'-t', 'syn[1,3,1]',
'-m', 'MeanSquares[data_dest.nii.gz,data_src.nii.gz,1,3]',
'-f', '2',
'-s', '0',
'-o', '[src2reg,data_src_reg.nii.gz]',
'-c', '5',
'-v', '1',
'-n', 'NearestNeighbor'], verbose, is_sct_binary=True)
# # Apply rigid transformation
# sct.printv('\nApply rigid transformation to curved landmarks...', verbose)
# sct.run('sct_apply_transfo -i data_src.nii.gz -o data_src_rigid.nii.gz -d data_dest.nii.gz -w curve2straight_rigid.txt -p nn', verbose)
#
# # Estimate b-spline transformation curve --> straight
# sct.printv('\nEstimate b-spline transformation: curve --> straight...', verbose)
# sct.run('isct_ANTSLandmarksBSplineTransform data_dest.nii.gz data_src_rigid.nii.gz warp_curve2straight_intermediate.nii.gz 5x5x5 3 2 0', verbose)
#
# # Concatenate rigid and non-linear transformations...
# sct.printv('\nConcatenate rigid and non-linear transformations...', verbose)
# cmd = 'isct_ComposeMultiTransform 3 warp_curve2straight.nii.gz -R data_dest.nii.gz warp_curve2straight_intermediate.nii.gz curve2straight_rigid.txt'
# sct.printv('>> '+cmd, verbose)
# sct.run(cmd)
#
# # Apply deformation to input image
# sct.printv('\nApply transformation to input image...', verbose)
# sct.run('sct_apply_transfo -i data_src.nii.gz -o data_src_warp.nii.gz -d data_dest.nii.gz -w warp_curve2straight.nii.gz -p nn', verbose)
#
# Compute DICE coefficient between src and dest
sct.printv('\nCompute DICE coefficient...', verbose)
sct.run(["sct_dice_coefficient",
"-i", "data_dest.nii.gz",
"-d", "data_src_reg.nii.gz",
"-o", "dice.txt"], verbose)
with open("dice.txt", "r") as file_dice:
dice = float(file_dice.read().replace('3D Dice coefficient = ', ''))
sct.printv('Dice coeff = ' + str(dice) + ' (should be above ' + str(dice_acceptable) + ')', verbose)
# Check if DICE coefficient is above acceptable value
if dice > dice_acceptable:
test_passed = 1
# come back
os.chdir(curdir)
# Delete temporary files
if remove_temp_files == 1:
sct.printv('\nDelete temporary files...', verbose)
sct.rmtree(path_tmp)
# output result for parent function
if test_passed:
sct.printv('\nTest passed!\n', verbose)
sys.exit(0)
else:
sct.printv('\nTest failed!\n', verbose)
sys.exit(1)
def main():
parser = get_parser()
param = Param()
arguments = parser.parse(sys.argv[1:])
# get arguments
fname_data = arguments['-i']
fname_seg = arguments['-s']
fname_landmarks = arguments['-l']
if '-ofolder' in arguments:
path_output = arguments['-ofolder']
else:
path_output = ''
path_template = sct.slash_at_the_end(arguments['-t'], 1)
contrast_template = arguments['-c']
remove_temp_files = int(arguments['-r'])
verbose = int(arguments['-v'])
if '-param-straighten' in arguments:
param.param_straighten = arguments['-param-straighten']
if 'cpu-nb' in arguments:
arg_cpu = ' -cpu-nb '+arguments['-cpu-nb']
else:
arg_cpu = ''
if '-param' in arguments:
paramreg_user = arguments['-param']
# update registration parameters
for paramStep in paramreg_user:
paramreg.addStep(paramStep)
# initialize other parameters
file_template_label = param.file_template_label
output_type = param.output_type
zsubsample = param.zsubsample
# smoothing_sigma = param.smoothing_sigma
# capitalize letters for contrast
if contrast_template == 't1':
contrast_template = 'T1'
elif contrast_template == 't2':
contrast_template = 'T2'
# retrieve file_template based on contrast
fname_template_list = glob(path_template+param.folder_template+'*'+contrast_template+'.nii.gz')
# TODO: make sure there is only one file -- check if file is there otherwise it crashes
fname_template = fname_template_list[0]
# retrieve file_template_seg
fname_template_seg_list = glob(path_template+param.folder_template+'*cord.nii.gz')
# TODO: make sure there is only one file
fname_template_seg = fname_template_seg_list[0]
# start timer
start_time = time.time()
# get absolute path - TO DO: remove! NEVER USE ABSOLUTE PATH...
path_template = os.path.abspath(path_template+param.folder_template)
# get fname of the template + template objects
# fname_template = sct.slash_at_the_end(path_template, 1)+file_template
fname_template_label = sct.slash_at_the_end(path_template, 1)+file_template_label
# fname_template_seg = sct.slash_at_the_end(path_template, 1)+file_template_seg
# check file existence
sct.printv('\nCheck template files...')
sct.check_file_exist(fname_template, verbose)
sct.check_file_exist(fname_template_label, verbose)
sct.check_file_exist(fname_template_seg, verbose)
# print arguments
sct.printv('\nCheck parameters:', verbose)
sct.printv('.. Data: '+fname_data, verbose)
sct.printv('.. Landmarks: '+fname_landmarks, verbose)
sct.printv('.. Segmentation: '+fname_seg, verbose)
sct.printv('.. Path template: '+path_template, verbose)
sct.printv('.. Path output: '+path_output, verbose)
sct.printv('.. Output type: '+str(output_type), verbose)
sct.printv('.. Remove temp files: '+str(remove_temp_files), verbose)
sct.printv('\nParameters for registration:')
for pStep in range(1, len(paramreg.steps)+1):
sct.printv('Step #'+paramreg.steps[str(pStep)].step, verbose)
sct.printv('.. Type #'+paramreg.steps[str(pStep)].type, verbose)
sct.printv('.. Algorithm................ '+paramreg.steps[str(pStep)].algo, verbose)
sct.printv('.. Metric................... '+paramreg.steps[str(pStep)].metric, verbose)
sct.printv('.. Number of iterations..... '+paramreg.steps[str(pStep)].iter, verbose)
sct.printv('.. Shrink factor............ '+paramreg.steps[str(pStep)].shrink, verbose)
sct.printv('.. Smoothing factor......... '+paramreg.steps[str(pStep)].smooth, verbose)
sct.printv('.. Gradient step............ '+paramreg.steps[str(pStep)].gradStep, verbose)
sct.printv('.. Degree of polynomial..... '+paramreg.steps[str(pStep)].poly, verbose)
path_data, file_data, ext_data = sct.extract_fname(fname_data)
sct.printv('\nCheck input labels...')
# check if label image contains coherent labels
image_label = Image(fname_landmarks)
# -> all labels must be different
labels = image_label.getNonZeroCoordinates(sorting='value')
hasDifferentLabels = True
for lab in labels:
for otherlabel in labels:
if lab != otherlabel and lab.hasEqualValue(otherlabel):
hasDifferentLabels = False
break
if not hasDifferentLabels:
sct.printv('ERROR: Wrong landmarks input. All labels must be different.', verbose, 'error')
# all labels must be available in tempalte
image_label_template = Image(fname_template_label)
labels_template = image_label_template.getNonZeroCoordinates(sorting='value')
if labels[-1].value > labels_template[-1].value:
sct.printv('ERROR: Wrong landmarks input. Labels must have correspondence in template space. \nLabel max '
'provided: ' + str(labels[-1].value) + '\nLabel max from template: ' +
str(labels_template[-1].value), verbose, 'error')
# create temporary folder
path_tmp = sct.tmp_create(verbose=verbose)
# set temporary file names
ftmp_data = 'data.nii'
ftmp_seg = 'seg.nii.gz'
ftmp_label = 'label.nii.gz'
ftmp_template = 'template.nii'
ftmp_template_seg = 'template_seg.nii.gz'
ftmp_template_label = 'template_label.nii.gz'
# copy files to temporary folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
sct.run('sct_convert -i '+fname_data+' -o '+path_tmp+ftmp_data)
sct.run('sct_convert -i '+fname_seg+' -o '+path_tmp+ftmp_seg)
sct.run('sct_convert -i '+fname_landmarks+' -o '+path_tmp+ftmp_label)
sct.run('sct_convert -i '+fname_template+' -o '+path_tmp+ftmp_template)
sct.run('sct_convert -i '+fname_template_seg+' -o '+path_tmp+ftmp_template_seg)
sct.run('sct_convert -i '+fname_template_label+' -o '+path_tmp+ftmp_template_label)
# go to tmp folder
os.chdir(path_tmp)
# smooth segmentation (jcohenadad, issue #613)
sct.printv('\nSmooth segmentation...', verbose)
sct.run('sct_maths -i '+ftmp_seg+' -smooth 1.5 -o '+add_suffix(ftmp_seg, '_smooth'))
ftmp_seg = add_suffix(ftmp_seg, '_smooth')
# resample data to 1mm isotropic
sct.printv('\nResample data to 1mm isotropic...', verbose)
sct.run('sct_resample -i '+ftmp_data+' -mm 1.0x1.0x1.0 -x linear -o '+add_suffix(ftmp_data, '_1mm'))
ftmp_data = add_suffix(ftmp_data, '_1mm')
sct.run('sct_resample -i '+ftmp_seg+' -mm 1.0x1.0x1.0 -x linear -o '+add_suffix(ftmp_seg, '_1mm'))
ftmp_seg = add_suffix(ftmp_seg, '_1mm')
# N.B. resampling of labels is more complicated, because they are single-point labels, therefore resampling with neighrest neighbour can make them disappear. Therefore a more clever approach is required.
resample_labels(ftmp_label, ftmp_data, add_suffix(ftmp_label, '_1mm'))
ftmp_label = add_suffix(ftmp_label, '_1mm')
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
sct.run('sct_image -i '+ftmp_data+' -setorient RPI -o '+add_suffix(ftmp_data, '_rpi'))
ftmp_data = add_suffix(ftmp_data, '_rpi')
sct.run('sct_image -i '+ftmp_seg+' -setorient RPI -o '+add_suffix(ftmp_seg, '_rpi'))
ftmp_seg = add_suffix(ftmp_seg, '_rpi')
sct.run('sct_image -i '+ftmp_label+' -setorient RPI -o '+add_suffix(ftmp_label, '_rpi'))
ftmp_label = add_suffix(ftmp_label, '_rpi')
# get landmarks in native space
# crop segmentation
# output: segmentation_rpi_crop.nii.gz
status_crop, output_crop = sct.run('sct_crop_image -i '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_crop')+' -dim 2 -bzmax', verbose)
ftmp_seg = add_suffix(ftmp_seg, '_crop')
cropping_slices = output_crop.split('Dimension 2: ')[1].split('\n')[0].split(' ')
# straighten segmentation
sct.printv('\nStraighten the spinal cord using centerline/segmentation...', verbose)
sct.run('sct_straighten_spinalcord -i '+ftmp_seg+' -s '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_straight')+' -qc 0 -r 0 -v '+str(verbose)+' '+param.param_straighten+arg_cpu, verbose)
# N.B. DO NOT UPDATE VARIABLE ftmp_seg BECAUSE TEMPORARY USED LATER
# re-define warping field using non-cropped space (to avoid issue #367)
sct.run('sct_concat_transfo -w warp_straight2curve.nii.gz -d '+ftmp_data+' -o warp_straight2curve.nii.gz')
# Label preparation:
# --------------------------------------------------------------------------------
# Remove unused label on template. Keep only label present in the input label image
sct.printv('\nRemove unused label on template. Keep only label present in the input label image...', verbose)
sct.run('sct_label_utils -p remove -i '+ftmp_template_label+' -o '+ftmp_template_label+' -r '+ftmp_label)
# Dilating the input label so they can be straighten without losing them
sct.printv('\nDilating input labels using 3vox ball radius')
sct.run('sct_maths -i '+ftmp_label+' -o '+add_suffix(ftmp_label, '_dilate')+' -dilate 3')
ftmp_label = add_suffix(ftmp_label, '_dilate')
# Apply straightening to labels
sct.printv('\nApply straightening to labels...', verbose)
sct.run('sct_apply_transfo -i '+ftmp_label+' -o '+add_suffix(ftmp_label, '_straight')+' -d '+add_suffix(ftmp_seg, '_straight')+' -w warp_curve2straight.nii.gz -x nn')
ftmp_label = add_suffix(ftmp_label, '_straight')
# Create crosses for the template labels and get coordinates
sct.printv('\nCreate a 15 mm cross for the template labels...', verbose)
template_image = Image(ftmp_template_label)
coordinates_input = template_image.getNonZeroCoordinates(sorting='value')
# jcohenadad, issue #628 <<<<<
# landmark_template = ProcessLabels.get_crosses_coordinates(coordinates_input, gapxy=15)
landmark_template = coordinates_input
# >>>>>
if verbose == 2:
# TODO: assign cross to image before saving
template_image.setFileName(add_suffix(ftmp_template_label, '_cross'))
template_image.save(type='minimize_int')
# Create crosses for the input labels into straight space and get coordinates
sct.printv('\nCreate a 15 mm cross for the input labels...', verbose)
label_straight_image = Image(ftmp_label)
coordinates_input = label_straight_image.getCoordinatesAveragedByValue() # landmarks are sorted by value
# jcohenadad, issue #628 <<<<<
# landmark_straight = ProcessLabels.get_crosses_coordinates(coordinates_input, gapxy=15)
landmark_straight = coordinates_input
# >>>>>
if verbose == 2:
# TODO: assign cross to image before saving
label_straight_image.setFileName(add_suffix(ftmp_label, '_cross'))
label_straight_image.save(type='minimize_int')
# Reorganize landmarks
points_fixed, points_moving = [], []
for coord in landmark_straight:
point_straight = label_straight_image.transfo_pix2phys([[coord.x, coord.y, coord.z]])
points_moving.append([point_straight[0][0], point_straight[0][1], point_straight[0][2]])
for coord in landmark_template:
point_template = template_image.transfo_pix2phys([[coord.x, coord.y, coord.z]])
points_fixed.append([point_template[0][0], point_template[0][1], point_template[0][2]])
# Register curved landmarks on straight landmarks based on python implementation
sct.printv('\nComputing rigid transformation (algo=translation-scaling-z) ...', verbose)
import msct_register_landmarks
# for some reason, the moving and fixed points are inverted between ITK transform and our python-based transform.
# and for another unknown reason, x and y dimensions have a negative sign (at least for translation and center of rotation).
if verbose == 2:
show_transfo = True
else:
show_transfo = False
(rotation_matrix, translation_array, points_moving_reg, points_moving_barycenter) = msct_register_landmarks.getRigidTransformFromLandmarks(points_moving, points_fixed, constraints='translation-scaling-z', show=show_transfo)
# writing rigid transformation file
text_file = open("straight2templateAffine.txt", "w")
text_file.write("#Insight Transform File V1.0\n")
text_file.write("#Transform 0\n")
text_file.write("Transform: AffineTransform_double_3_3\n")
text_file.write("Parameters: %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f %.9f\n" % (
rotation_matrix[0, 0], rotation_matrix[0, 1], rotation_matrix[0, 2],
rotation_matrix[1, 0], rotation_matrix[1, 1], rotation_matrix[1, 2],
rotation_matrix[2, 0], rotation_matrix[2, 1], rotation_matrix[2, 2],
-translation_array[0, 0], -translation_array[0, 1], translation_array[0, 2]))
text_file.write("FixedParameters: %.9f %.9f %.9f\n" % (-points_moving_barycenter[0],
-points_moving_barycenter[1],
points_moving_barycenter[2]))
text_file.close()
# Concatenate transformations: curve --> straight --> affine
sct.printv('\nConcatenate transformations: curve --> straight --> affine...', verbose)
sct.run('sct_concat_transfo -w warp_curve2straight.nii.gz,straight2templateAffine.txt -d template.nii -o warp_curve2straightAffine.nii.gz')
# Apply transformation
sct.printv('\nApply transformation...', verbose)
sct.run('sct_apply_transfo -i '+ftmp_data+' -o '+add_suffix(ftmp_data, '_straightAffine')+' -d '+ftmp_template+' -w warp_curve2straightAffine.nii.gz')
ftmp_data = add_suffix(ftmp_data, '_straightAffine')
sct.run('sct_apply_transfo -i '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_straightAffine')+' -d '+ftmp_template+' -w warp_curve2straightAffine.nii.gz -x linear')
ftmp_seg = add_suffix(ftmp_seg, '_straightAffine')
# threshold and binarize
sct.printv('\nBinarize segmentation...', verbose)
sct.run('sct_maths -i '+ftmp_seg+' -thr 0.4 -o '+add_suffix(ftmp_seg, '_thr'))
sct.run('sct_maths -i '+add_suffix(ftmp_seg, '_thr')+' -bin -o '+add_suffix(ftmp_seg, '_thr_bin'))
ftmp_seg = add_suffix(ftmp_seg, '_thr_bin')
# find min-max of anat2template (for subsequent cropping)
zmin_template, zmax_template = find_zmin_zmax(ftmp_seg)
# crop template in z-direction (for faster processing)
sct.printv('\nCrop data in template space (for faster processing)...', verbose)
sct.run('sct_crop_image -i '+ftmp_template+' -o '+add_suffix(ftmp_template, '_crop')+' -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
ftmp_template = add_suffix(ftmp_template, '_crop')
sct.run('sct_crop_image -i '+ftmp_template_seg+' -o '+add_suffix(ftmp_template_seg, '_crop')+' -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
ftmp_template_seg = add_suffix(ftmp_template_seg, '_crop')
sct.run('sct_crop_image -i '+ftmp_data+' -o '+add_suffix(ftmp_data, '_crop')+' -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
ftmp_data = add_suffix(ftmp_data, '_crop')
sct.run('sct_crop_image -i '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_crop')+' -dim 2 -start '+str(zmin_template)+' -end '+str(zmax_template))
ftmp_seg = add_suffix(ftmp_seg, '_crop')
# sub-sample in z-direction
sct.printv('\nSub-sample in z-direction (for faster processing)...', verbose)
sct.run('sct_resample -i '+ftmp_template+' -o '+add_suffix(ftmp_template, '_sub')+' -f 1x1x'+zsubsample, verbose)
ftmp_template = add_suffix(ftmp_template, '_sub')
sct.run('sct_resample -i '+ftmp_template_seg+' -o '+add_suffix(ftmp_template_seg, '_sub')+' -f 1x1x'+zsubsample, verbose)
ftmp_template_seg = add_suffix(ftmp_template_seg, '_sub')
sct.run('sct_resample -i '+ftmp_data+' -o '+add_suffix(ftmp_data, '_sub')+' -f 1x1x'+zsubsample, verbose)
ftmp_data = add_suffix(ftmp_data, '_sub')
sct.run('sct_resample -i '+ftmp_seg+' -o '+add_suffix(ftmp_seg, '_sub')+' -f 1x1x'+zsubsample, verbose)
ftmp_seg = add_suffix(ftmp_seg, '_sub')
# Registration straight spinal cord to template
sct.printv('\nRegister straight spinal cord to template...', verbose)
# loop across registration steps
warp_forward = []
warp_inverse = []
for i_step in range(1, len(paramreg.steps)+1):
sct.printv('\nEstimate transformation for step #'+str(i_step)+'...', verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_data
dest = ftmp_template
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_seg
dest = ftmp_template_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# if step>1, apply warp_forward_concat to the src image to be used
if i_step > 1:
# sct.run('sct_apply_transfo -i '+src+' -d '+dest+' -w '+','.join(warp_forward)+' -o '+sct.add_suffix(src, '_reg')+' -x '+interp_step, verbose)
sct.run('sct_apply_transfo -i '+src+' -d '+dest+' -w '+','.join(warp_forward)+' -o '+add_suffix(src, '_reg')+' -x '+interp_step, verbose)
src = add_suffix(src, '_reg')
# register src --> dest
warp_forward_out, warp_inverse_out = register(src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.append(warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: anat --> template...', verbose)
sct.run('sct_concat_transfo -w warp_curve2straightAffine.nii.gz,'+','.join(warp_forward)+' -d template.nii -o warp_anat2template.nii.gz', verbose)
# sct.run('sct_concat_transfo -w warp_curve2straight.nii.gz,straight2templateAffine.txt,'+','.join(warp_forward)+' -d template.nii -o warp_anat2template.nii.gz', verbose)
sct.printv('\nConcatenate transformations: template --> anat...', verbose)
warp_inverse.reverse()
sct.run('sct_concat_transfo -w '+','.join(warp_inverse)+',-straight2templateAffine.txt,warp_straight2curve.nii.gz -d data.nii -o warp_template2anat.nii.gz', verbose)
# Apply warping fields to anat and template
if output_type == 1:
sct.run('sct_apply_transfo -i template.nii -o template2anat.nii.gz -d data.nii -w warp_template2anat.nii.gz -crop 1', verbose)
sct.run('sct_apply_transfo -i data.nii -o anat2template.nii.gz -d template.nii -w warp_anat2template.nii.gz -crop 1', verbose)
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', verbose)
sct.generate_output_file(path_tmp+'warp_template2anat.nii.gz', path_output+'warp_template2anat.nii.gz', verbose)
sct.generate_output_file(path_tmp+'warp_anat2template.nii.gz', path_output+'warp_anat2template.nii.gz', verbose)
if output_type == 1:
sct.generate_output_file(path_tmp+'template2anat.nii.gz', path_output+'template2anat'+ext_data, verbose)
sct.generate_output_file(path_tmp+'anat2template.nii.gz', path_output+'anat2template'+ext_data, verbose)
# Delete temporary files
if remove_temp_files:
sct.printv('\nDelete temporary files...', verbose)
sct.run('rm -rf '+path_tmp)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: '+str(int(round(elapsed_time)))+'s', verbose)
# to view results
sct.printv('\nTo view results, type:', verbose)
sct.printv('fslview '+fname_data+' '+path_output+'template2anat -b 0,4000 &', verbose, 'info')
sct.printv('fslview '+fname_template+' -b 0,5000 '+path_output+'anat2template &\n', verbose, 'info')
def main(args=None):
if args is None:
args = sys.argv[1:]
# get parser
parser = get_parser()
arguments = parser.parse(args)
if '-d' in arguments:
param.download = int(arguments['-d'])
if '-p' in arguments:
param.path_data = arguments['-p']
if '-f' in arguments:
param.function_to_test = arguments['-f']
if '-r' in arguments:
param.remove_tmp_file = int(arguments['-r'])
# path_data = param.path_data
function_to_test = param.function_to_test
# function_to_avoid = param.function_to_avoid
remove_tmp_file = param.remove_tmp_file
start_time = time.time()
# # download data
# if param.download:
# downloaddata()
#
# get absolute path and add slash at the end
param.path_data = sct.slash_at_the_end(os.path.abspath(param.path_data), 1)
# check existence of testing data folder
if not os.path.isdir(param.path_data) or param.download:
downloaddata()
# display path to data
sct.printv('\nPath to testing data: '+param.path_data, param.verbose)
# create temp folder that will have all results and go in it
param.path_tmp = sct.tmp_create()
# param.path_tmp = sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
# sct.create_folder(param.path_tmp)
os.chdir(param.path_tmp)
# get list of all scripts to test
functions = fill_functions()
# loop across all functions and test them
status = []
[status.append(test_function(f)) for f in functions if function_to_test == f]
if not status:
for f in functions:
status.append(test_function(f))
print 'status: '+str(status)
# display elapsed time
elapsed_time = time.time() - start_time
print 'Finished! Elapsed time: '+str(int(round(elapsed_time)))+'s\n'
# remove temp files
if param.remove_tmp_file:
sct.printv('\nRemove temporary files...', param.verbose)
sct.run('rm -rf '+param.path_tmp, param.verbose)
e = 0
if sum(status) != 0:
e = 1
print e
sys.exit(e)
def eddy_correct(param):
sct.printv('\n\n\n\n===================================================', param.verbose)
sct.printv(' Running: eddy_correct', param.verbose)
sct.printv('===================================================\n', param.verbose)
path_tmp = sct.tmp_create()
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
fname_data = param.fname_data
min_norm = param.min_norm
cost_function = param.cost_function_flirt
verbose = param.verbose
sct.printv(('Input File:' + param.fname_data), verbose)
sct.printv(('Bvecs File:' + param.fname_bvecs), verbose)
# Extract path, file and extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
if param.mat_eddy == '':
param.mat_eddy = 'mat_eddy'
if not os.path.exists(param.mat_eddy):
os.makedirs(param.mat_eddy)
mat_eddy = param.mat_eddy
# Schedule file for FLIRT
schedule_file = os.path.join(path_sct , 'flirtsch', 'schedule_TxTy_2mmScale.sch')
sct.printv(('\n.. Schedule file: ' + schedule_file), verbose)
# Swap X-Y dimension (to have X as phase-encoding direction)
if param.swapXY == 1:
sct.printv('\nSwap X-Y dimension (to have X as phase-encoding direction)', verbose)
fname_data_new = 'tmp.data_swap'
cmd = [fsloutput, 'fslswapdim', fname_data, '-y', '-x', '-z', fname_data_new]
status, output = sct.run(cmd, verbose)
sct.printv(('\n.. updated data file name: ' + fname_data_new), verbose)
else:
fname_data_new = fname_data
# Get size of data
sct.printv('\nGet dimensions data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image(fname_data).dim
sct.printv('.. ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt), verbose)
# split along T dimension
sct.printv('\nSplit along T dimension...', verbose)
from sct_image import split_data
im_to_split = Image(fname_data_new + '.nii')
im_split_list = split_data(im_to_split, 3)
for im in im_split_list:
im.save()
# cmd = [fsloutput, 'fslsplit', fname_data_new, file_data + '_T']
# status, output = sct.run(cmd,verbose)
# Slice-wise or Volume based method
if param.slicewise:
nb_loops = nz
file_suffix = []
for iZ in range(nz):
file_suffix.append('_Z' + str(iZ).zfill(4))
else:
nb_loops = 1
file_suffix = ['']
# Identify pairs of opposite gradient directions
sct.printv('\nIdentify pairs of opposite gradient directions...', verbose)
# Open bvecs file
sct.printv('\nOpen bvecs file...', verbose)
bvecs = []
with open(param.fname_bvecs) as f:
for line in f:
bvecs_new = list(map(float, line.split()))
bvecs.append(bvecs_new)
# Check if bvecs file is nx3
if not len(bvecs[0][:]) == 3:
sct.printv('.. WARNING: bvecs file is 3xn instead of nx3. Consider using sct_dmri_transpose_bvecs.', verbose)
sct.printv('Transpose bvecs...', verbose)
# transpose bvecs
bvecs = list(zip(*bvecs))
bvecs = np.array(bvecs)
opposite_gradients_iT = []
opposite_gradients_jT = []
index_identified = []
index_b0 = []
for iT in range(nt - 1):
if np.linalg.norm(bvecs[iT, :]) != 0:
if iT not in index_identified:
jT = iT + 1
if np.linalg.norm((bvecs[iT, :] + bvecs[jT, :])) < min_norm:
sct.printv(('.. Opposite gradient for #' + str(iT) + ' is: #' + str(jT)), verbose)
opposite_gradients_iT.append(iT)
opposite_gradients_jT.append(jT)
index_identified.append(iT)
else:
index_b0.append(iT)
sct.printv(('.. Opposite gradient for #' + str(iT) + ' is: NONE (b=0)'), verbose)
nb_oppositeGradients = len(opposite_gradients_iT)
sct.printv(('.. Number of gradient directions: ' + str(2 * nb_oppositeGradients) + ' (2*' + str(nb_oppositeGradients) + ')'), verbose)
sct.printv('.. Index b=0: ' + str(index_b0), verbose)
# =========================================================================
# Find transformation
# =========================================================================
for iN in range(nb_oppositeGradients):
i_plus = opposite_gradients_iT[iN]
i_minus = opposite_gradients_jT[iN]
sct.printv(('\nFinding affine transformation between volumes #' + str(i_plus) + ' and #' + str(i_minus) + ' (' + str(iN) + '/' + str(nb_oppositeGradients) + ')'), verbose)
sct.printv('------------------------------------------------------------------------------------\n', verbose)
# Slicewise correction
if param.slicewise:
sct.printv('\nSplit volumes across Z...', verbose)
fname_plus = file_data + '_T' + str(i_plus).zfill(4)
fname_plus_Z = file_data + '_T' + str(i_plus).zfill(4) + '_Z'
im_plus = Image(fname_plus + '.nii')
im_plus_split_list = split_data(im_plus, 2)
for im_p in im_plus_split_list:
im_p.save()
# cmd = fsloutput + 'fslsplit ' + fname_plus + ' ' + fname_plus_Z + ' -z'
# status, output = sct.run(cmd,verbose)
fname_minus = file_data + '_T' + str(i_minus).zfill(4)
fname_minus_Z = file_data + '_T' + str(i_minus).zfill(4) + '_Z'
im_minus = Image(fname_minus + '.nii')
im_minus_split_list = split_data(im_minus, 2)
for im_m in im_minus_split_list:
im_m.save() # cmd = fsloutput + 'fslsplit ' + fname_minus + ' ' + fname_minus_Z + ' -z'
# status, output = sct.run(cmd,verbose)
# loop across Z
for iZ in range(nb_loops):
fname_plus = file_data + '_T' + str(i_plus).zfill(4) + file_suffix[iZ]
fname_minus = file_data + '_T' + str(i_minus).zfill(4) + file_suffix[iZ]
# Find transformation on opposite gradient directions
sct.printv('\nFind transformation for each pair of opposite gradient directions...', verbose)
fname_plus_corr = file_data + '_T' + str(i_plus).zfill(4) + file_suffix[iZ] + '_corr_'
omat = 'mat_' + file_data + '_T' + str(i_plus).zfill(4) + file_suffix[iZ] + '.txt'
cmd = [fsloutput, 'flirt', '-in', fname_plus, '-ref', fname_minus, '-paddingsize', '3', '-schedule', schedule_file, '-verbose', '2', '-omat', omat, '-cost', cost_function, '-forcescaling']
status, output = sct.run(cmd, verbose)
file = open(omat)
Matrix = np.loadtxt(file)
file.close()
M = Matrix[0:4, 0:4]
sct.printv(('.. Transformation matrix:\n' + str(M)), verbose)
sct.printv(('.. Output matrix file: ' + omat), verbose)
# Divide affine transformation by two
sct.printv('\nDivide affine transformation by two...', verbose)
A = (M - np.identity(4)) / 2
Mplus = np.identity(4) + A
omat_plus = os.path.join(mat_eddy, 'mat.T' + str(i_plus) + '_Z' + str(iZ) + '.txt')
file = open(omat_plus, 'w')
np.savetxt(omat_plus, Mplus, fmt='%.6e', delimiter=' ', newline='\n', header='', footer='', comments='#')
file.close()
sct.printv(('.. Output matrix file (plus): ' + omat_plus), verbose)
Mminus = np.identity(4) - A
omat_minus = os.path.join(mat_eddy, 'mat.T' + str(i_minus) + '_Z' + str(iZ) + '.txt')
file = open(omat_minus, 'w')
np.savetxt(omat_minus, Mminus, fmt='%.6e', delimiter=' ', newline='\n', header='', footer='', comments='#')
file.close()
sct.printv(('.. Output matrix file (minus): ' + omat_minus), verbose)
# =========================================================================
# Apply affine transformation
# =========================================================================
sct.printv('\nApply affine transformation matrix', verbose)
sct.printv('------------------------------------------------------------------------------------\n', verbose)
for iN in range(nb_oppositeGradients):
for iFile in range(2):
if iFile == 0:
i_file = opposite_gradients_iT[iN]
else:
i_file = opposite_gradients_jT[iN]
for iZ in range(nb_loops):
fname = file_data + '_T' + str(i_file).zfill(4) + file_suffix[iZ]
fname_corr = fname + '_corr_' + '__div2'
omat = os.path.join(mat_eddy, 'mat.T' + str(i_file) + '_Z' + str(iZ) + '.txt')
cmd = [fsloutput, 'flirt', '-in', fname, '-ref', fname, '-out', fname_corr, '-init', omat, '-applyxfm', '-paddingsize', '3', '-interp', param.interp]
status, output = sct.run(cmd, verbose)
# =========================================================================
# Merge back across Z
# =========================================================================
sct.printv('\nMerge across Z', verbose)
sct.printv('------------------------------------------------------------------------------------\n', verbose)
for iN in range(nb_oppositeGradients):
i_plus = opposite_gradients_iT[iN]
fname_plus_corr = file_data + '_T' + str(i_plus).zfill(4) + '_corr_' + '__div2'
cmd = [fsloutput, 'fslmerge', '-z', fname_plus_corr]
for iZ in range(nz):
fname_plus_Z_corr = file_data + '_T' + str(i_plus).zfill(4) + file_suffix[iZ] + '_corr_' + '__div2'
cmd = cmd + [fname_plus_Z_corr]
status, output = sct.run(cmd, verbose)
i_minus = opposite_gradients_jT[iN]
fname_minus_corr = file_data + '_T' + str(i_minus).zfill(4) + '_corr_' + '__div2'
cmd = [fsloutput, 'fslmerge', '-z', fname_minus_corr]
for iZ in range(nz):
fname_minus_Z_corr = file_data + '_T' + str(i_minus).zfill(4) + file_suffix[iZ] + '_corr_' + '__div2'
cmd = cmd + [fname_minus_Z_corr]
status, output = sct.run(cmd, verbose)
# =========================================================================
# Merge files back
# =========================================================================
sct.printv('\nMerge back across T...', verbose)
sct.printv('------------------------------------------------------------------------------------\n', verbose)
fname_data_corr = os.path.join(param.output_path, file_data + '_eddy')
cmd = [fsloutput, 'fslmerge', '-t', fname_data_corr]
for iT in range(nt):
if os.path.isfile((os.path.join(path_tmp, file_data) + '_T' + str(iT).zfill(4) + '_corr_' + '__div2.nii')):
fname_data_corr_3d = file_data + '_T' + str(iT).zfill(4) + '_corr_' + '__div2'
elif iT in index_b0:
fname_data_corr_3d = file_data + '_T' + str(iT).zfill(4)
cmd = cmd + [fname_data_corr_3d]
status, output = sct.run(cmd, verbose)
# Swap back X-Y dimensions
if param.swapXY == 1:
fname_data_final = fname_data
sct.printv('\nSwap back X-Y dimensions', verbose)
cmd = [fsloutput, 'fslswapdim', fname_data_corr, '-y', '-x', '-z', fname_data_final]
status, output = sct.run(cmd, verbose)
else:
fname_data_final = fname_data_corr
sct.printv(('... File created: ' + fname_data_final), verbose)
sct.printv('\n===================================================', verbose)
sct.printv(' Completed: eddy_correct', verbose)
sct.printv('===================================================\n\n\n', verbose)
# come back
os.chdir(curdir)
# Delete temporary files
if param.delete_tmp_files == 1:
sct.printv('\nDelete temporary files...')
sct.rmtree(path_tmp)
def main(args=None):
# initialization
start_time = time.time()
path_out = '.'
param = Param()
# reducing the number of CPU used for moco (see issue #201)
os.environ["ITK_GLOBAL_DEFAULT_NUMBER_OF_THREADS"] = "1"
# get path of the toolbox
# status, param.path_sct = sct.run('echo $SCT_DIR')
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
param.fname_data = arguments['-i']
param.fname_bvecs = arguments['-bvec']
if '-bval' in arguments:
param.fname_bvals = arguments['-bval']
if '-bvalmin' in arguments:
param.bval_min = arguments['-bvalmin']
if '-g' in arguments:
param.group_size = arguments['-g']
if '-m' in arguments:
param.fname_mask = arguments['-m']
if '-param' in arguments:
param.update(arguments['-param'])
if '-thr' in arguments:
param.otsu = arguments['-thr']
if '-x' in arguments:
param.interp = arguments['-x']
if '-ofolder' in arguments:
path_out = arguments['-ofolder']
if '-r' in arguments:
param.remove_temp_files = int(arguments['-r'])
if '-v' in arguments:
param.verbose = int(arguments['-v'])
# Get full path
param.fname_data = os.path.abspath(param.fname_data)
param.fname_bvecs = os.path.abspath(param.fname_bvecs)
if param.fname_bvals != '':
param.fname_bvals = os.path.abspath(param.fname_bvals)
if param.fname_mask != '':
param.fname_mask = os.path.abspath(param.fname_mask)
# Extract path, file and extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
path_mask, file_mask, ext_mask = sct.extract_fname(param.fname_mask)
path_tmp = sct.tmp_create(basename="dmri_moco", verbose=param.verbose)
# names of files in temporary folder
ext = '.nii'
dmri_name = 'dmri'
mask_name = 'mask'
bvecs_fname = 'bvecs.txt'
# Copying input data to tmp folder
sct.printv('\nCopying input data to tmp folder and convert to nii...',
param.verbose)
convert(param.fname_data, os.path.join(path_tmp, dmri_name + ext))
sct.copy(param.fname_bvecs,
os.path.join(path_tmp, bvecs_fname),
verbose=param.verbose)
if param.fname_mask != '':
sct.copy(param.fname_mask,
os.path.join(path_tmp, mask_name + ext_mask),
verbose=param.verbose)
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# update field in param (because used later).
# TODO: make this cleaner...
if param.fname_mask != '':
param.fname_mask = mask_name + ext_mask
# run moco
dmri_moco(param)
# come back
os.chdir(curdir)
# Generate output files
fname_dmri_moco = os.path.join(path_out,
file_data + param.suffix + ext_data)
sct.create_folder(path_out)
sct.printv('\nGenerate output files...', param.verbose)
sct.generate_output_file(
os.path.join(path_tmp, dmri_name + param.suffix + ext),
os.path.join(path_out, file_data + param.suffix + ext_data),
param.verbose)
sct.generate_output_file(
os.path.join(path_tmp, "b0_mean.nii"),
os.path.join(path_out, 'b0' + param.suffix + '_mean' + ext_data),
param.verbose)
sct.generate_output_file(
os.path.join(path_tmp, "dwi_mean.nii"),
os.path.join(path_out, 'dwi' + param.suffix + '_mean' + ext_data),
param.verbose)
# Delete temporary files
if param.remove_temp_files == 1:
sct.printv('\nDelete temporary files...', param.verbose)
sct.rmtree(path_tmp, verbose=param.verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv(
'\nFinished! Elapsed time: ' + str(int(round(elapsed_time))) + 's',
param.verbose)
sct.display_viewer_syntax([fname_dmri_moco, file_data], mode='ortho,ortho')
def create_mask():
fsloutput = 'export FSLOUTPUTTYPE=NIFTI; ' # for faster processing, all outputs are in NIFTI
# parse argument for method
method_type = param.process[0]
# check method val
if not method_type == 'center':
method_val = param.process[1]
# check existence of input files
if method_type == 'centerline':
sct.check_file_exist(method_val, param.verbose)
# Extract path/file/extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
# Get output folder and file name
if param.fname_out == '':
param.fname_out = param.file_prefix+file_data+ext_data
# create temporary folder
sct.printv('\nCreate temporary folder...', param.verbose)
path_tmp = sct.tmp_create(param.verbose)
# )sct.slash_at_the_end('tmp.'+time.strftime("%y%m%d%H%M%S"), 1)
# sct.run('mkdir '+path_tmp, param.verbose)
sct.printv('\nCheck orientation...', param.verbose)
orientation_input = get_orientation(Image(param.fname_data))
sct.printv('.. '+orientation_input, param.verbose)
reorient_coordinates = False
# copy input data to tmp folder
convert(param.fname_data, path_tmp+'data.nii')
if method_type == 'centerline':
convert(method_val, path_tmp+'centerline.nii.gz')
if method_type == 'point':
convert(method_val, path_tmp+'point.nii.gz')
# go to tmp folder
os.chdir(path_tmp)
# reorient to RPI
sct.printv('\nReorient to RPI...', param.verbose)
# if not orientation_input == 'RPI':
sct.run('sct_image -i data.nii -o data_RPI.nii -setorient RPI -v 0', verbose=False)
if method_type == 'centerline':
sct.run('sct_image -i centerline.nii.gz -o centerline_RPI.nii.gz -setorient RPI -v 0', verbose=False)
if method_type == 'point':
sct.run('sct_image -i point.nii.gz -o point_RPI.nii.gz -setorient RPI -v 0', verbose=False)
#
# if method_type == 'centerline':
# orientation_centerline = get_orientation_3d(method_val, filename=True)
# if not orientation_centerline == 'RPI':
# sct.run('sct_image -i ' + method_val + ' -o ' + path_tmp + 'centerline.nii.gz' + ' -setorient RPI -v 0', verbose=False)
# else:
# convert(method_val, path_tmp+'centerline.nii.gz')
# Get dimensions of data
sct.printv('\nGet dimensions of data...', param.verbose)
nx, ny, nz, nt, px, py, pz, pt = Image('data_RPI.nii').dim
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz)+ ' x ' + str(nt), param.verbose)
# in case user input 4d data
if nt != 1:
sct.printv('WARNING in '+os.path.basename(__file__)+': Input image is 4d but output mask will 3D.', param.verbose, 'warning')
# extract first volume to have 3d reference
nii = Image('data_RPI.nii')
data3d = nii.data[:,:,:,0]
nii.data = data3d
nii.save()
if method_type == 'coord':
# parse to get coordinate
coord = map(int, method_val.split('x'))
if method_type == 'point':
# get file name
fname_point = method_val
# extract coordinate of point
sct.printv('\nExtract coordinate of point...', param.verbose)
# TODO: change this way to remove dependence to sct.run. ProcessLabels.display_voxel returns list of coordinates
status, output = sct.run('sct_label_utils -i point_RPI.nii.gz -display', param.verbose)
# parse to get coordinate
coord = output[output.find('Position=')+10:-17].split(',')
if method_type == 'center':
# set coordinate at center of FOV
coord = round(float(nx)/2), round(float(ny)/2)
if method_type == 'centerline':
# get name of centerline from user argument
fname_centerline = 'centerline_RPI.nii.gz'
else:
# generate volume with line along Z at coordinates 'coord'
sct.printv('\nCreate line...', param.verbose)
fname_centerline = create_line('data_RPI.nii', coord, nz)
# create mask
sct.printv('\nCreate mask...', param.verbose)
centerline = nibabel.load(fname_centerline) # open centerline
hdr = centerline.get_header() # get header
hdr.set_data_dtype('uint8') # set imagetype to uint8
spacing = hdr.structarr['pixdim']
data_centerline = centerline.get_data() # get centerline
z_centerline_not_null = [iz for iz in range(0, nz, 1) if data_centerline[:, :, iz].any()]
# get center of mass of the centerline
cx = [0] * nz
cy = [0] * nz
for iz in range(0, nz, 1):
if iz in z_centerline_not_null:
cx[iz], cy[iz] = ndimage.measurements.center_of_mass(numpy.array(data_centerline[:, :, iz]))
# create 2d masks
file_mask = 'data_mask'
for iz in range(nz):
if iz not in z_centerline_not_null:
# write an empty nifty volume
img = nibabel.Nifti1Image(data_centerline[:, :, iz], None, hdr)
nibabel.save(img, (file_mask + str(iz) + '.nii'))
else:
center = numpy.array([cx[iz], cy[iz]])
mask2d = create_mask2d(center, param.shape, param.size, nx, ny, even=param.even, spacing=spacing)
# Write NIFTI volumes
img = nibabel.Nifti1Image(mask2d, None, hdr)
nibabel.save(img, (file_mask+str(iz)+'.nii'))
# merge along Z
# cmd = 'fslmerge -z mask '
# CHANGE THAT CAN IMPACT SPEED:
# related to issue #755, we cannot open more than 256 files at one time.
# to solve this issue, we do not open more than 100 files
'''
im_list = []
im_temp = []
for iz in range(nz_not_null):
if iz != 0 and iz % 100 == 0:
im_temp.append(concat_data(im_list, 2))
im_list = [Image(file_mask + str(iz) + '.nii')]
else:
im_list.append(Image(file_mask+str(iz)+'.nii'))
if im_temp:
im_temp.append(concat_data(im_list, 2))
im_out = concat_data(im_temp, 2, no_expand=True)
else:
im_out = concat_data(im_list, 2)
'''
fname_list = [file_mask + str(iz) + '.nii' for iz in range(nz)]
im_out = concat_data(fname_list, dim=2)
im_out.setFileName('mask_RPI.nii.gz')
im_out.save()
# reorient if necessary
# if not orientation_input == 'RPI':
sct.run('sct_image -i mask_RPI.nii.gz -o mask.nii.gz -setorient ' + orientation_input, param.verbose)
# copy header input --> mask
im_dat = Image('data.nii')
im_mask = Image('mask.nii.gz')
im_mask = copy_header(im_dat, im_mask)
im_mask.save()
# come back to parent folder
os.chdir('..')
# Generate output files
sct.printv('\nGenerate output files...', param.verbose)
sct.generate_output_file(path_tmp+'mask.nii.gz', param.fname_out)
# Remove temporary files
if param.remove_tmp_files == 1:
sct.printv('\nRemove temporary files...', param.verbose)
sct.run('rm -rf '+path_tmp, param.verbose, error_exit='warning')
# to view results
sct.printv('\nDone! To view results, type:', param.verbose)
sct.printv('fslview '+param.fname_data+' '+param.fname_out+' -l Red -t 0.5 &', param.verbose, 'info')
print
def apply(self):
# Initialization
fname_src = self.input_filename # source image (moving)
list_warp = self.list_warp # list of warping fields
fname_out = self.output_filename # output
fname_dest = self.fname_dest # destination image (fix)
verbose = self.verbose
remove_temp_files = self.remove_temp_files
crop_reference = self.crop # if = 1, put 0 everywhere around warping field, if = 2, real crop
islabel = False
if self.interp == 'label':
islabel = True
self.interp = 'nn'
interp = sct.get_interpolation('isct_antsApplyTransforms', self.interp)
# Parse list of warping fields
sct.printv('\nParse list of warping fields...', verbose)
use_inverse = []
fname_warp_list_invert = []
# list_warp = list_warp.replace(' ', '') # remove spaces
# list_warp = list_warp.split(",") # parse with comma
for idx_warp, path_warp in enumerate(self.list_warp):
# Check if this transformation should be inverted
if path_warp in self.list_warpinv:
use_inverse.append('-i')
# list_warp[idx_warp] = path_warp[1:] # remove '-'
fname_warp_list_invert += [[use_inverse[idx_warp], list_warp[idx_warp]]]
else:
use_inverse.append('')
fname_warp_list_invert += [[path_warp]]
path_warp = list_warp[idx_warp]
if path_warp.endswith((".nii", ".nii.gz")) \
and Image(list_warp[idx_warp]).header.get_intent()[0] != 'vector':
raise ValueError("Displacement field in {} is invalid: should be encoded" \
" in a 5D file with vector intent code" \
" (see https://nifti.nimh.nih.gov/pub/dist/src/niftilib/nifti1.h" \
.format(path_warp))
# need to check if last warping field is an affine transfo
isLastAffine = False
path_fname, file_fname, ext_fname = sct.extract_fname(fname_warp_list_invert[-1][-1])
if ext_fname in ['.txt', '.mat']:
isLastAffine = True
# check if destination file is 3d
if not sct.check_if_3d(fname_dest):
sct.printv('ERROR: Destination data must be 3d')
# N.B. Here we take the inverse of the warp list, because sct_WarpImageMultiTransform concatenates in the reverse order
fname_warp_list_invert.reverse()
fname_warp_list_invert = functools.reduce(lambda x, y: x + y, fname_warp_list_invert)
# Extract path, file and extension
path_src, file_src, ext_src = sct.extract_fname(fname_src)
path_dest, file_dest, ext_dest = sct.extract_fname(fname_dest)
# Get output folder and file name
if fname_out == '':
path_out = '' # output in user's current directory
file_out = file_src + '_reg'
ext_out = ext_src
fname_out = os.path.join(path_out, file_out + ext_out)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
img_src = Image(fname_src)
nx, ny, nz, nt, px, py, pz, pt = img_src.dim
# nx, ny, nz, nt, px, py, pz, pt = sct.get_dimension(fname_src)
sct.printv(' ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz) + ' x ' + str(nt), verbose)
# if 3d
if nt == 1:
# Apply transformation
sct.printv('\nApply transformation...', verbose)
if nz in [0, 1]:
dim = '2'
else:
dim = '3'
# if labels, dilate before resampling
if islabel:
sct.printv("\nDilate labels before warping...")
path_tmp = sct.tmp_create(basename="apply_transfo", verbose=verbose)
fname_dilated_labels = os.path.join(path_tmp, "dilated_data.nii")
# dilate points
sct.run(['sct_maths',
'-i', fname_src,
'-o', fname_dilated_labels,
'-dilate', '2'])
fname_src = fname_dilated_labels
sct.printv("\nApply transformation and resample to destination space...", verbose)
sct.run(['isct_antsApplyTransforms',
'-d', dim,
'-i', fname_src,
'-o', fname_out,
'-t'
] + fname_warp_list_invert + ['-r', fname_dest] + interp, verbose=verbose, is_sct_binary=True)
# if 4d, loop across the T dimension
else:
if islabel:
raise NotImplementedError
dim = '4'
path_tmp = sct.tmp_create(basename="apply_transfo", verbose=verbose)
# convert to nifti into temp folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
img_src.save(os.path.join(path_tmp, "data.nii"))
sct.copy(fname_dest, os.path.join(path_tmp, file_dest + ext_dest))
fname_warp_list_tmp = []
for fname_warp in list_warp:
path_warp, file_warp, ext_warp = sct.extract_fname(fname_warp)
sct.copy(fname_warp, os.path.join(path_tmp, file_warp + ext_warp))
fname_warp_list_tmp.append(file_warp + ext_warp)
fname_warp_list_invert_tmp = fname_warp_list_tmp[::-1]
curdir = os.getcwd()
os.chdir(path_tmp)
# split along T dimension
sct.printv('\nSplit along T dimension...', verbose)
im_dat = Image('data.nii')
im_header = im_dat.hdr
data_split_list = sct_image.split_data(im_dat, 3)
for im in data_split_list:
im.save()
# apply transfo
sct.printv('\nApply transformation to each 3D volume...', verbose)
for it in range(nt):
file_data_split = 'data_T' + str(it).zfill(4) + '.nii'
file_data_split_reg = 'data_reg_T' + str(it).zfill(4) + '.nii'
status, output = sct.run(['isct_antsApplyTransforms',
'-d', '3',
'-i', file_data_split,
'-o', file_data_split_reg,
'-t',
] + fname_warp_list_invert_tmp + [
'-r', file_dest + ext_dest,
] + interp, verbose, is_sct_binary=True)
# Merge files back
sct.printv('\nMerge file back...', verbose)
import glob
path_out, name_out, ext_out = sct.extract_fname(fname_out)
# im_list = [Image(file_name) for file_name in glob.glob('data_reg_T*.nii')]
# concat_data use to take a list of image in input, now takes a list of file names to open the files one by one (see issue #715)
fname_list = glob.glob('data_reg_T*.nii')
fname_list.sort()
im_out = sct_image.concat_data(fname_list, 3, im_header['pixdim'])
im_out.save(name_out + ext_out)
os.chdir(curdir)
sct.generate_output_file(os.path.join(path_tmp, name_out + ext_out), fname_out)
# Delete temporary folder if specified
if remove_temp_files:
sct.printv('\nRemove temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# Copy affine matrix from destination space to make sure qform/sform are the same
sct.printv("Copy affine matrix from destination space to make sure qform/sform are the same.", verbose)
im_src_reg = Image(fname_out)
im_src_reg.copy_qform_from_ref(Image(fname_dest))
im_src_reg.save(verbose=0) # set verbose=0 to avoid warning message about rewriting file
if islabel:
sct.printv("\nTake the center of mass of each registered dilated labels...")
sct.run(['sct_label_utils',
'-i', fname_out,
'-o', fname_out,
'-cubic-to-point'])
if remove_temp_files:
sct.printv('\nRemove temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# 2. crop the resulting image using dimensions from the warping field
warping_field = fname_warp_list_invert[-1]
# if last warping field is an affine transfo, we need to compute the space of the concatenate warping field:
if isLastAffine:
sct.printv('WARNING: the resulting image could have wrong apparent results. You should use an affine transformation as last transformation...', verbose, 'warning')
else:
if crop_reference in [1, 2]:
# Extract only the first ndim of the warping field
img_warp = Image(warping_field)
if dim == '2':
img_warp_ndim = Image(img_src.data[:, :], hdr=img_warp.hdr)
elif dim in ['3', '4']:
img_warp_ndim = Image(img_src.data[:, :, :], hdr=img_warp.hdr)
# Set zero to everything outside the warping field
cropper = ImageCropper(Image(fname_out))
cropper.get_bbox_from_ref(img_warp_ndim)
if crop_reference == 1:
img_out = cropper.crop(background=0)
elif crop_reference == 2:
img_out = cropper.crop()
img_out.save(fname_out)
sct.display_viewer_syntax([fname_dest, fname_out], verbose=verbose)
def main(args=None):
# initialization
start_time = time.time()
param = Param()
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
param.fname_data = arguments['-i']
if '-g' in arguments:
param.group_size = arguments['-g']
if '-m' in arguments:
param.fname_mask = arguments['-m']
if '-param' in arguments:
param.update(arguments['-param'])
if '-x' in arguments:
param.interp = arguments['-x']
if '-ofolder' in arguments:
path_out = arguments['-ofolder']
if '-r' in arguments:
param.remove_temp_files = int(arguments['-r'])
param.verbose = int(arguments.get('-v'))
sct.init_sct(log_level=param.verbose, update=True) # Update log level
sct.printv('\nInput parameters:', param.verbose)
sct.printv(' input file ............' + param.fname_data, param.verbose)
# Get full path
param.fname_data = os.path.abspath(param.fname_data)
if param.fname_mask != '':
param.fname_mask = os.path.abspath(param.fname_mask)
# Extract path, file and extension
path_data, file_data, ext_data = sct.extract_fname(param.fname_data)
path_tmp = sct.tmp_create(basename="fmri_moco", verbose=param.verbose)
# Copying input data to tmp folder and convert to nii
# TODO: no need to do that (takes time for nothing)
sct.printv('\nCopying input data to tmp folder and convert to nii...', param.verbose)
convert(param.fname_data, os.path.join(path_tmp, "fmri.nii"), squeeze_data=False)
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# run moco
fmri_moco(param)
# come back
os.chdir(curdir)
# Generate output files
fname_fmri_moco = os.path.join(path_out, file_data + param.suffix + ext_data)
sct.create_folder(path_out)
sct.printv('\nGenerate output files...', param.verbose)
sct.generate_output_file(os.path.join(path_tmp, "fmri" + param.suffix + '.nii'), fname_fmri_moco, param.verbose)
sct.generate_output_file(os.path.join(path_tmp, "fmri" + param.suffix + '_mean.nii'), os.path.join(path_out, file_data + param.suffix + '_mean' + ext_data), param.verbose)
# Delete temporary files
if param.remove_temp_files == 1:
sct.printv('\nDelete temporary files...', param.verbose)
sct.rmtree(path_tmp, verbose=param.verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's', param.verbose)
sct.display_viewer_syntax([fname_fmri_moco, file_data], mode='ortho,ortho')
def main(args=None):
# initializations
param = Param()
# check user arguments
if not args:
args = sys.argv[1:]
# Get parser info
parser = get_parser()
arguments = parser.parse(args)
fname_data = arguments['-i']
fname_seg = arguments['-s']
if '-l' in arguments:
fname_landmarks = arguments['-l']
label_type = 'body'
elif '-ldisc' in arguments:
fname_landmarks = arguments['-ldisc']
label_type = 'disc'
else:
sct.printv('ERROR: Labels should be provided.', 1, 'error')
if '-ofolder' in arguments:
path_output = arguments['-ofolder']
else:
path_output = ''
param.path_qc = arguments.get("-qc", None)
path_template = arguments['-t']
contrast_template = arguments['-c']
ref = arguments['-ref']
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 '-straighten-fitting' in arguments:
param.straighten_fitting = arguments['-straighten-fitting']
# if '-cpu-nb' in arguments:
# arg_cpu = ' -cpu-nb '+str(arguments['-cpu-nb'])
# else:
# arg_cpu = ''
# registration parameters
if '-param' in arguments:
# reset parameters but keep step=0 (might be overwritten if user specified step=0)
paramreg = ParamregMultiStep([step0])
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# add user parameters
for paramStep in arguments['-param']:
paramreg.addStep(paramStep)
else:
paramreg = ParamregMultiStep([step0, step1, step2])
# if ref=subject, initialize registration using different affine parameters
if ref == 'subject':
paramreg.steps['0'].dof = 'Tx_Ty_Tz_Rx_Ry_Rz_Sz'
# initialize other parameters
zsubsample = param.zsubsample
# retrieve template file names
file_template_vertebral_labeling = get_file_label(
os.path.join(path_template, 'template'), 'vertebral labeling')
file_template = get_file_label(
os.path.join(path_template, 'template'),
contrast_template.upper() + '-weighted template')
file_template_seg = get_file_label(os.path.join(path_template, 'template'),
'spinal cord')
# start timer
start_time = time.time()
# get fname of the template + template objects
fname_template = os.path.join(path_template, 'template', file_template)
fname_template_vertebral_labeling = os.path.join(
path_template, 'template', file_template_vertebral_labeling)
fname_template_seg = os.path.join(path_template, 'template',
file_template_seg)
fname_template_disc_labeling = os.path.join(path_template, 'template',
'PAM50_label_disc.nii.gz')
# check file existence
# TODO: no need to do that!
sct.printv('\nCheck template files...')
sct.check_file_exist(fname_template, verbose)
sct.check_file_exist(fname_template_vertebral_labeling, verbose)
sct.check_file_exist(fname_template_seg, verbose)
path_data, file_data, ext_data = sct.extract_fname(fname_data)
# sct.printv(arguments)
sct.printv('\nCheck parameters:', verbose)
sct.printv(' Data: ' + fname_data, verbose)
sct.printv(' Landmarks: ' + fname_landmarks, verbose)
sct.printv(' Segmentation: ' + fname_seg, verbose)
sct.printv(' Path template: ' + path_template, verbose)
sct.printv(' Remove temp files: ' + str(remove_temp_files), verbose)
# check input labels
labels = check_labels(fname_landmarks, label_type=label_type)
vertebral_alignment = False
if len(labels) > 2 and label_type == 'disc':
vertebral_alignment = True
path_tmp = sct.tmp_create(basename="register_to_template", verbose=verbose)
# set temporary file names
ftmp_data = 'data.nii'
ftmp_seg = 'seg.nii.gz'
ftmp_label = 'label.nii.gz'
ftmp_template = 'template.nii'
ftmp_template_seg = 'template_seg.nii.gz'
ftmp_template_label = 'template_label.nii.gz'
# copy files to temporary folder
sct.printv('\nCopying input data to tmp folder and convert to nii...',
verbose)
Image(fname_data).save(os.path.join(path_tmp, ftmp_data))
Image(fname_seg).save(os.path.join(path_tmp, ftmp_seg))
Image(fname_landmarks).save(os.path.join(path_tmp, ftmp_label))
Image(fname_template).save(os.path.join(path_tmp, ftmp_template))
Image(fname_template_seg).save(os.path.join(path_tmp, ftmp_template_seg))
Image(fname_template_vertebral_labeling).save(
os.path.join(path_tmp, ftmp_template_label))
if label_type == 'disc':
Image(fname_template_disc_labeling).save(
os.path.join(path_tmp, ftmp_template_label))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Generate labels from template vertebral labeling
if label_type == 'body':
sct.printv('\nGenerate labels from template vertebral labeling',
verbose)
ftmp_template_label_, ftmp_template_label = ftmp_template_label, sct.add_suffix(
ftmp_template_label, "_body")
sct_label_utils.main(args=[
'-i', ftmp_template_label_, '-vert-body', '0', '-o',
ftmp_template_label
])
# check if provided labels are available in the template
sct.printv('\nCheck if provided labels are available in the template',
verbose)
image_label_template = Image(ftmp_template_label)
labels_template = image_label_template.getNonZeroCoordinates(
sorting='value')
if labels[-1].value > labels_template[-1].value:
sct.printv(
'ERROR: Wrong landmarks input. Labels must have correspondence in template space. \nLabel max '
'provided: ' + str(labels[-1].value) +
'\nLabel max from template: ' + str(labels_template[-1].value),
verbose, 'error')
# if only one label is present, force affine transformation to be Tx,Ty,Tz only (no scaling)
if len(labels) == 1:
paramreg.steps['0'].dof = 'Tx_Ty_Tz'
sct.printv(
'WARNING: Only one label is present. Forcing initial transformation to: '
+ paramreg.steps['0'].dof, 1, 'warning')
# Project labels onto the spinal cord centerline because later, an affine transformation is estimated between the
# template's labels (centered in the cord) and the subject's labels (assumed to be centered in the cord).
# If labels are not centered, mis-registration errors are observed (see issue #1826)
ftmp_label = project_labels_on_spinalcord(ftmp_label, ftmp_seg)
# binarize segmentation (in case it has values below 0 caused by manual editing)
sct.printv('\nBinarize segmentation', verbose)
ftmp_seg_, ftmp_seg = ftmp_seg, sct.add_suffix(ftmp_seg, "_bin")
sct.run(['sct_maths', '-i', ftmp_seg_, '-bin', '0.5', '-o', ftmp_seg])
# Switch between modes: subject->template or template->subject
if ref == 'template':
# resample data to 1mm isotropic
sct.printv('\nResample data to 1mm isotropic...', verbose)
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 nearest neighbour can make them disappear.
resample_labels(ftmp_label, ftmp_data, add_suffix(ftmp_label, '_1mm'))
ftmp_label = add_suffix(ftmp_label, '_1mm')
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
ftmp_data = Image(ftmp_data).change_orientation(
"RPI", generate_path=True).save().absolutepath
ftmp_seg = Image(ftmp_seg).change_orientation(
"RPI", generate_path=True).save().absolutepath
ftmp_label = Image(ftmp_label).change_orientation(
"RPI", generate_path=True).save().absolutepath
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_crop')
if vertebral_alignment:
# cropping the segmentation based on the label coverage to ensure good registration with vertebral alignment
# See https://github.com/neuropoly/spinalcordtoolbox/pull/1669 for details
image_labels = Image(ftmp_label)
coordinates_labels = image_labels.getNonZeroCoordinates(
sorting='z')
nx, ny, nz, nt, px, py, pz, pt = image_labels.dim
offset_crop = 10.0 * pz # cropping the image 10 mm above and below the highest and lowest label
cropping_slices = [
coordinates_labels[0].z - offset_crop,
coordinates_labels[-1].z + offset_crop
]
# make sure that the cropping slices do not extend outside of the slice range (issue #1811)
if cropping_slices[0] < 0:
cropping_slices[0] = 0
if cropping_slices[1] > nz:
cropping_slices[1] = nz
msct_image.spatial_crop(
Image(ftmp_seg_),
dict(((2,
np.int32(np.round(cropping_slices))), ))).save(ftmp_seg)
else:
# if we do not align the vertebral levels, we crop the segmentation from top to bottom
im_seg_rpi = Image(ftmp_seg_)
bottom = 0
for data in msct_image.SlicerOneAxis(im_seg_rpi, "IS"):
if (data != 0).any():
break
bottom += 1
top = im_seg_rpi.data.shape[2]
for data in msct_image.SlicerOneAxis(im_seg_rpi, "SI"):
if (data != 0).any():
break
top -= 1
msct_image.spatial_crop(im_seg_rpi, dict(
((2, (bottom, top)), ))).save(ftmp_seg)
# straighten segmentation
sct.printv(
'\nStraighten the spinal cord using centerline/segmentation...',
verbose)
# check if warp_curve2straight and warp_straight2curve already exist (i.e. no need to do it another time)
fn_warp_curve2straight = os.path.join(curdir,
"warp_curve2straight.nii.gz")
fn_warp_straight2curve = os.path.join(curdir,
"warp_straight2curve.nii.gz")
fn_straight_ref = os.path.join(curdir, "straight_ref.nii.gz")
cache_input_files = [ftmp_seg]
if vertebral_alignment:
cache_input_files += [
ftmp_template_seg,
ftmp_label,
ftmp_template_label,
]
cache_sig = sct.cache_signature(input_files=cache_input_files, )
cachefile = os.path.join(curdir, "straightening.cache")
if sct.cache_valid(
cachefile, cache_sig
) and os.path.isfile(fn_warp_curve2straight) and os.path.isfile(
fn_warp_straight2curve) and os.path.isfile(fn_straight_ref):
sct.printv(
'Reusing existing warping field which seems to be valid',
verbose, 'warning')
sct.copy(fn_warp_curve2straight, 'warp_curve2straight.nii.gz')
sct.copy(fn_warp_straight2curve, 'warp_straight2curve.nii.gz')
sct.copy(fn_straight_ref, 'straight_ref.nii.gz')
# apply straightening
sct.run([
'sct_apply_transfo', '-i', ftmp_seg, '-w',
'warp_curve2straight.nii.gz', '-d', 'straight_ref.nii.gz',
'-o',
add_suffix(ftmp_seg, '_straight')
])
else:
from sct_straighten_spinalcord import SpinalCordStraightener
sc_straight = SpinalCordStraightener(ftmp_seg, ftmp_seg)
sc_straight.algo_fitting = param.straighten_fitting
sc_straight.output_filename = add_suffix(ftmp_seg, '_straight')
sc_straight.path_output = './'
sc_straight.qc = '0'
sc_straight.remove_temp_files = remove_temp_files
sc_straight.verbose = verbose
if vertebral_alignment:
sc_straight.centerline_reference_filename = ftmp_template_seg
sc_straight.use_straight_reference = True
sc_straight.discs_input_filename = ftmp_label
sc_straight.discs_ref_filename = ftmp_template_label
sc_straight.straighten()
sct.cache_save(cachefile, cache_sig)
# N.B. DO NOT UPDATE VARIABLE ftmp_seg BECAUSE TEMPORARY USED LATER
# re-define warping field using non-cropped space (to avoid issue #367)
s, o = sct.run([
'sct_concat_transfo', '-w', 'warp_straight2curve.nii.gz', '-d',
ftmp_data, '-o', 'warp_straight2curve.nii.gz'
])
if vertebral_alignment:
sct.copy('warp_curve2straight.nii.gz',
'warp_curve2straightAffine.nii.gz')
else:
# Label preparation:
# --------------------------------------------------------------------------------
# Remove unused label on template. Keep only label present in the input label image
sct.printv(
'\nRemove unused label on template. Keep only label present in the input label image...',
verbose)
sct.run([
'sct_label_utils', '-i', ftmp_template_label, '-o',
ftmp_template_label, '-remove', 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://www.slideshare.net/neuropoly/sct-course-20190121/42',
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(np.round(np.min(points_straight))), int(np.round(np.max(points_straight)))
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_black')
msct_image.spatial_crop(Image(ftmp_seg_), dict(((2, (min_point,max_point)),))).save(ftmp_seg)
"""
# open segmentation
im = Image(ftmp_seg)
im_new = msct_image.empty_like(im)
# binarize
im_new.data = im.data > 0.5
# find min-max of anat2template (for subsequent cropping)
zmin_template, zmax_template = msct_image.find_zmin_zmax(im_new,
threshold=0.5)
# save binarized segmentation
im_new.save(add_suffix(ftmp_seg, '_bin')) # unused?
# crop template in z-direction (for faster processing)
# TODO: refactor to use python module instead of doing i/o
sct.printv('\nCrop data in template space (for faster processing)...',
verbose)
ftmp_template_, ftmp_template = ftmp_template, add_suffix(
ftmp_template, '_crop')
msct_image.spatial_crop(Image(ftmp_template_),
dict(
((2,
(zmin_template,
zmax_template)), ))).save(ftmp_template)
ftmp_template_seg_, ftmp_template_seg = ftmp_template_seg, add_suffix(
ftmp_template_seg, '_crop')
msct_image.spatial_crop(
Image(ftmp_template_seg_),
dict(((2, (zmin_template,
zmax_template)), ))).save(ftmp_template_seg)
ftmp_data_, ftmp_data = ftmp_data, add_suffix(ftmp_data, '_crop')
msct_image.spatial_crop(Image(ftmp_data_),
dict(((2, (zmin_template,
zmax_template)), ))).save(ftmp_data)
ftmp_seg_, ftmp_seg = ftmp_seg, add_suffix(ftmp_seg, '_crop')
msct_image.spatial_crop(Image(ftmp_seg_),
dict(((2, (zmin_template,
zmax_template)), ))).save(ftmp_seg)
# sub-sample in z-direction
# TODO: refactor to use python module instead of doing i/o
sct.printv('\nSub-sample in z-direction (for faster processing)...',
verbose)
sct.run([
'sct_resample', '-i', ftmp_template, '-o',
add_suffix(ftmp_template, '_sub'), '-f', '1x1x' + zsubsample
], verbose)
ftmp_template = add_suffix(ftmp_template, '_sub')
sct.run([
'sct_resample', '-i', ftmp_template_seg, '-o',
add_suffix(ftmp_template_seg, '_sub'), '-f', '1x1x' + zsubsample
], verbose)
ftmp_template_seg = add_suffix(ftmp_template_seg, '_sub')
sct.run([
'sct_resample', '-i', ftmp_data, '-o',
add_suffix(ftmp_data, '_sub'), '-f', '1x1x' + zsubsample
], verbose)
ftmp_data = add_suffix(ftmp_data, '_sub')
sct.run([
'sct_resample', '-i', ftmp_seg, '-o',
add_suffix(ftmp_seg, '_sub'), '-f', '1x1x' + zsubsample
], verbose)
ftmp_seg = add_suffix(ftmp_seg, '_sub')
# Registration straight spinal cord to template
sct.printv('\nRegister straight spinal cord to template...', verbose)
# loop across registration steps
warp_forward = []
warp_inverse = []
for i_step in range(1, len(paramreg.steps)):
sct.printv(
'\nEstimate transformation for step #' + str(i_step) + '...',
verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_data
dest = ftmp_template
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_seg
dest = ftmp_template_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# if 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()
if vertebral_alignment:
sct.run([
'sct_concat_transfo', '-w',
','.join(warp_inverse) + ',warp_straight2curve.nii.gz', '-d',
'data.nii', '-o', 'warp_template2anat.nii.gz'
], verbose)
else:
sct.run([
'sct_concat_transfo', '-w', ','.join(warp_inverse) +
',-straight2templateAffine.txt,warp_straight2curve.nii.gz',
'-d', 'data.nii', '-o', 'warp_template2anat.nii.gz'
], verbose)
# register template->subject
elif ref == 'subject':
# Change orientation of input images to RPI
sct.printv('\nChange orientation of input images to RPI...', verbose)
ftmp_data = Image(ftmp_data).change_orientation(
"RPI", generate_path=True).save().absolutepath
ftmp_seg = Image(ftmp_seg).change_orientation(
"RPI", generate_path=True).save().absolutepath
ftmp_label = Image(ftmp_label).change_orientation(
"RPI", generate_path=True).save().absolutepath
# Remove unused label on template. Keep only label present in the input label image
sct.printv(
'\nRemove unused label on template. Keep only label present in the input label image...',
verbose)
sct.run([
'sct_label_utils', '-i', ftmp_template_label, '-o',
ftmp_template_label, '-remove', ftmp_label
])
# Add one label because at least 3 orthogonal labels are required to estimate an affine transformation. This
# new label is added at the level of the upper most label (lowest value), at 1cm to the right.
for i_file in [ftmp_label, ftmp_template_label]:
im_label = Image(i_file)
coord_label = im_label.getCoordinatesAveragedByValue(
) # N.B. landmarks are sorted by value
# Create new label
from copy import deepcopy
new_label = deepcopy(coord_label[0])
# move it 5mm to the left (orientation is RAS)
nx, ny, nz, nt, px, py, pz, pt = im_label.dim
new_label.x = np.round(coord_label[0].x + 5.0 / px)
# assign value 99
new_label.value = 99
# Add to existing image
im_label.data[int(new_label.x),
int(new_label.y),
int(new_label.z)] = new_label.value
# Overwrite label file
# im_label.absolutepath = 'label_rpi_modif.nii.gz'
im_label.save()
# Bring template to subject space using landmark-based transformation
sct.printv('\nEstimate transformation for step #0...', verbose)
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="./")
except Exception:
sct.printv(
'ERROR: input labels do not seem to be at the right place. Please check the position of the labels. See documentation for more details: https://www.slideshare.net/neuropoly/sct-course-20190121/42',
verbose=verbose,
type='error')
# loop across registration steps
for i_step in range(1, len(paramreg.steps)):
sct.printv(
'\nEstimate transformation for step #' + str(i_step) + '...',
verbose)
# identify which is the src and dest
if paramreg.steps[str(i_step)].type == 'im':
src = ftmp_template
dest = ftmp_data
interp_step = 'linear'
elif paramreg.steps[str(i_step)].type == 'seg':
src = ftmp_template_seg
dest = ftmp_seg
interp_step = 'nn'
else:
sct.printv('ERROR: Wrong image type.', 1, 'error')
# apply transformation from previous step, to use as new src for registration
sct.run([
'sct_apply_transfo', '-i', src, '-d', dest, '-w',
','.join(warp_forward), '-o',
add_suffix(src,
'_regStep' + str(i_step - 1)), '-x', interp_step
], verbose)
src = add_suffix(src, '_regStep' + str(i_step - 1))
# register src --> dest
# TODO: display param for debugging
warp_forward_out, warp_inverse_out = register(
src, dest, paramreg, param, str(i_step))
warp_forward.append(warp_forward_out)
warp_inverse.insert(0, warp_inverse_out)
# Concatenate transformations:
sct.printv('\nConcatenate transformations: template --> subject...',
verbose)
sct.run([
'sct_concat_transfo', '-w', ','.join(warp_forward), '-d',
'data.nii', '-o', 'warp_template2anat.nii.gz'
], verbose)
sct.printv('\nConcatenate transformations: subject --> template...',
verbose)
sct.run([
'sct_concat_transfo', '-w', ','.join(warp_inverse), '-d',
'template.nii', '-o', 'warp_anat2template.nii.gz'
], verbose)
# Apply warping fields to anat and template
sct.run([
'sct_apply_transfo', '-i', 'template.nii', '-o',
'template2anat.nii.gz', '-d', 'data.nii', '-w',
'warp_template2anat.nii.gz', '-crop', '1'
], verbose)
sct.run([
'sct_apply_transfo', '-i', 'data.nii', '-o', 'anat2template.nii.gz',
'-d', 'template.nii', '-w', 'warp_anat2template.nii.gz', '-crop', '1'
], verbose)
# come back
os.chdir(curdir)
# Generate output files
sct.printv('\nGenerate output files...', verbose)
fname_template2anat = os.path.join(path_output, 'template2anat' + ext_data)
fname_anat2template = os.path.join(path_output, 'anat2template' + ext_data)
sct.generate_output_file(
os.path.join(path_tmp, "warp_template2anat.nii.gz"),
os.path.join(path_output, "warp_template2anat.nii.gz"), verbose)
sct.generate_output_file(
os.path.join(path_tmp, "warp_anat2template.nii.gz"),
os.path.join(path_output, "warp_anat2template.nii.gz"), verbose)
sct.generate_output_file(os.path.join(path_tmp, "template2anat.nii.gz"),
fname_template2anat, verbose)
sct.generate_output_file(os.path.join(path_tmp, "anat2template.nii.gz"),
fname_anat2template, verbose)
if ref == 'template':
# copy straightening files in case subsequent SCT functions need them
sct.generate_output_file(
os.path.join(path_tmp, "warp_curve2straight.nii.gz"),
os.path.join(path_output, "warp_curve2straight.nii.gz"), verbose)
sct.generate_output_file(
os.path.join(path_tmp, "warp_straight2curve.nii.gz"),
os.path.join(path_output, "warp_straight2curve.nii.gz"), verbose)
sct.generate_output_file(
os.path.join(path_tmp, "straight_ref.nii.gz"),
os.path.join(path_output, "straight_ref.nii.gz"), verbose)
# Delete temporary files
if remove_temp_files:
sct.printv('\nDelete temporary files...', verbose)
sct.rmtree(path_tmp, verbose=verbose)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv(
'\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's',
verbose)
if param.path_qc is not None:
generate_qc(fname_data,
fname_in2=fname_template2anat,
fname_seg=fname_seg,
args=args,
path_qc=os.path.abspath(param.path_qc),
process='sct_register_to_template')
sct.display_viewer_syntax([fname_data, fname_template2anat],
verbose=verbose)
sct.display_viewer_syntax([fname_template, fname_anat2template],
verbose=verbose)
def main(args=None):
# Initialization
param = Param()
start_time = time.time()
parser = get_parser()
arguments = parser.parse(sys.argv[1:])
fname_anat = arguments['-i']
fname_centerline = arguments['-s']
if '-smooth' in arguments:
sigma = arguments['-smooth']
if '-param' in arguments:
param.update(arguments['-param'])
if '-r' in arguments:
remove_temp_files = int(arguments['-r'])
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
# Display arguments
sct.printv('\nCheck input arguments...')
sct.printv(' Volume to smooth .................. ' + fname_anat)
sct.printv(' Centerline ........................ ' + fname_centerline)
sct.printv(' Sigma (mm) ........................ ' + str(sigma))
sct.printv(' Verbose ........................... ' + str(verbose))
# Check that input is 3D:
from spinalcordtoolbox.image import Image
nx, ny, nz, nt, px, py, pz, pt = Image(fname_anat).dim
dim = 4 # by default, will be adjusted later
if nt == 1:
dim = 3
if nz == 1:
dim = 2
if dim == 4:
sct.printv('WARNING: the input image is 4D, please split your image to 3D before smoothing spinalcord using :\n'
'sct_image -i ' + fname_anat + ' -split t -o ' + fname_anat, verbose, 'warning')
sct.printv('4D images not supported, aborting ...', verbose, 'error')
# Extract path/file/extension
path_anat, file_anat, ext_anat = sct.extract_fname(fname_anat)
path_centerline, file_centerline, ext_centerline = sct.extract_fname(fname_centerline)
path_tmp = sct.tmp_create(basename="smooth_spinalcord", verbose=verbose)
# Copying input data to tmp folder
sct.printv('\nCopying input data to tmp folder and convert to nii...', verbose)
sct.copy(fname_anat, os.path.join(path_tmp, "anat" + ext_anat))
sct.copy(fname_centerline, os.path.join(path_tmp, "centerline" + ext_centerline))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# convert to nii format
convert('anat' + ext_anat, 'anat.nii')
convert('centerline' + ext_centerline, 'centerline.nii')
# Change orientation of the input image into RPI
sct.printv('\nOrient input volume to RPI orientation...')
fname_anat_rpi = msct_image.Image("anat.nii") \
.change_orientation("RPI", generate_path=True) \
.save() \
.absolutepath
# Change orientation of the input image into RPI
sct.printv('\nOrient centerline to RPI orientation...')
fname_centerline_rpi = msct_image.Image("centerline.nii") \
.change_orientation("RPI", generate_path=True) \
.save() \
.absolutepath
# Straighten the spinal cord
# straighten segmentation
sct.printv('\nStraighten the spinal cord using centerline/segmentation...', verbose)
cache_sig = sct.cache_signature(input_files=[fname_anat_rpi, fname_centerline_rpi],
input_params={"x": "spline"})
cachefile = os.path.join(curdir, "straightening.cache")
if sct.cache_valid(cachefile, cache_sig) and os.path.isfile(os.path.join(curdir, 'warp_curve2straight.nii.gz')) and os.path.isfile(os.path.join(curdir, 'warp_straight2curve.nii.gz')) and os.path.isfile(os.path.join(curdir, 'straight_ref.nii.gz')):
# if they exist, copy them into current folder
sct.printv('Reusing existing warping field which seems to be valid', verbose, 'warning')
sct.copy(os.path.join(curdir, 'warp_curve2straight.nii.gz'), 'warp_curve2straight.nii.gz')
sct.copy(os.path.join(curdir, 'warp_straight2curve.nii.gz'), 'warp_straight2curve.nii.gz')
sct.copy(os.path.join(curdir, 'straight_ref.nii.gz'), 'straight_ref.nii.gz')
# apply straightening
sct.run(['sct_apply_transfo', '-i', fname_anat_rpi, '-w', 'warp_curve2straight.nii.gz', '-d', 'straight_ref.nii.gz', '-o', 'anat_rpi_straight.nii', '-x', 'spline'], verbose)
else:
sct.run(['sct_straighten_spinalcord', '-i', fname_anat_rpi, '-o', 'anat_rpi_straight.nii', '-s', fname_centerline_rpi, '-x', 'spline', '-param', 'algo_fitting='+param.algo_fitting], verbose)
sct.cache_save(cachefile, cache_sig)
# move warping fields locally (to use caching next time)
sct.copy('warp_curve2straight.nii.gz', os.path.join(curdir, 'warp_curve2straight.nii.gz'))
sct.copy('warp_straight2curve.nii.gz', os.path.join(curdir, 'warp_straight2curve.nii.gz'))
# Smooth the straightened image along z
sct.printv('\nSmooth the straightened image...')
sigma_smooth = ",".join([str(i) for i in sigma])
sct_maths.main(args=['-i', 'anat_rpi_straight.nii',
'-smooth', sigma_smooth,
'-o', 'anat_rpi_straight_smooth.nii',
'-v', '0'])
# Apply the reversed warping field to get back the curved spinal cord
sct.printv('\nApply the reversed warping field to get back the curved spinal cord...')
sct.run(['sct_apply_transfo', '-i', 'anat_rpi_straight_smooth.nii', '-o', 'anat_rpi_straight_smooth_curved.nii', '-d', 'anat.nii', '-w', 'warp_straight2curve.nii.gz', '-x', 'spline'], verbose)
# replace zeroed voxels by original image (issue #937)
sct.printv('\nReplace zeroed voxels by original image...', verbose)
nii_smooth = Image('anat_rpi_straight_smooth_curved.nii')
data_smooth = nii_smooth.data
data_input = Image('anat.nii').data
indzero = np.where(data_smooth == 0)
data_smooth[indzero] = data_input[indzero]
nii_smooth.data = data_smooth
nii_smooth.save('anat_rpi_straight_smooth_curved_nonzero.nii')
# come back
os.chdir(curdir)
# Generate output file
sct.printv('\nGenerate output file...')
sct.generate_output_file(os.path.join(path_tmp, "anat_rpi_straight_smooth_curved_nonzero.nii"),
file_anat + '_smooth' + ext_anat)
# Remove temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...')
sct.rmtree(path_tmp)
# Display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' + str(int(np.round(elapsed_time))) + 's\n')
sct.display_viewer_syntax([file_anat, file_anat + '_smooth'], verbose=verbose)
def main():
# Initialization
fname_data = ''
interp_factor = param.interp_factor
remove_temp_files = param.remove_temp_files
verbose = param.verbose
suffix = param.suffix
smoothing_sigma = param.smoothing_sigma
# start timer
start_time = time.time()
# get path of the toolbox
path_sct = os.environ.get("SCT_DIR",
os.path.dirname(os.path.dirname(__file__)))
# Parameters for debug mode
if param.debug:
fname_data = os.path.join(path_sct, 'testing', 'data', 'errsm_23',
't2', 't2_manual_segmentation.nii.gz')
remove_temp_files = 0
param.mask_size = 10
else:
# Check input parameters
try:
opts, args = getopt.getopt(sys.argv[1:], 'hi:v:r:s:')
except getopt.GetoptError:
usage()
if not opts:
usage()
for opt, arg in opts:
if opt == '-h':
usage()
elif opt in ('-i'):
fname_data = arg
elif opt in ('-r'):
remove_temp_files = int(arg)
elif opt in ('-s'):
smoothing_sigma = arg
elif opt in ('-v'):
verbose = int(arg)
# display usage if a mandatory argument is not provided
if fname_data == '':
usage()
# sct.printv(arguments)
sct.printv('\nCheck parameters:')
sct.printv(' segmentation ........... ' + fname_data)
sct.printv(' interp factor .......... ' + str(interp_factor))
sct.printv(' smoothing sigma ........ ' + str(smoothing_sigma))
# check existence of input files
sct.printv('\nCheck existence of input files...')
sct.check_file_exist(fname_data, verbose)
# Extract path, file and extension
path_data, file_data, ext_data = sct.extract_fname(fname_data)
path_tmp = sct.tmp_create(basename="binary_to_trilinear", verbose=verbose)
from sct_convert import convert
sct.printv('\nCopying input data to tmp folder and convert to nii...',
param.verbose)
convert(fname_data, os.path.join(path_tmp, "data.nii"))
# go to tmp folder
curdir = os.getcwd()
os.chdir(path_tmp)
# Get dimensions of data
sct.printv('\nGet dimensions of data...', verbose)
nx, ny, nz, nt, px, py, pz, pt = Image('data.nii').dim
sct.printv('.. ' + str(nx) + ' x ' + str(ny) + ' x ' + str(nz), verbose)
# upsample data
sct.printv('\nUpsample data...', verbose)
sct.run([
"sct_resample", "-i", "data.nii", "-x", "linear", "-vox",
str(nx * interp_factor) + 'x' + str(ny * interp_factor) + 'x' +
str(nz * interp_factor), "-o", "data_up.nii"
], verbose)
# Smooth along centerline
sct.printv('\nSmooth along centerline...', verbose)
sct.run([
"sct_smooth_spinalcord", "-i", "data_up.nii", "-s", "data_up.nii",
"-smooth",
str(smoothing_sigma), "-r",
str(remove_temp_files), "-v",
str(verbose)
], verbose)
# downsample data
sct.printv('\nDownsample data...', verbose)
sct.run([
"sct_resample", "-i", "data_up_smooth.nii", "-x", "linear", "-vox",
str(nx) + 'x' + str(ny) + 'x' + str(nz), "-o",
"data_up_smooth_down.nii"
], verbose)
# come back
os.chdir(curdir)
# Generate output files
sct.printv('\nGenerate output files...')
fname_out = sct.generate_output_file(
os.path.join(path_tmp, "data_up_smooth_down.nii"),
'' + file_data + suffix + ext_data)
# Delete temporary files
if remove_temp_files == 1:
sct.printv('\nRemove temporary files...')
sct.rmtree(path_tmp)
# display elapsed time
elapsed_time = time.time() - start_time
sct.printv('\nFinished! Elapsed time: ' +
str(int(np.round(elapsed_time))) + 's')
# to view results
sct.printv('\nTo view results, type:')
sct.printv('fslview ' + file_data + ' ' + file_data + suffix + ' &\n')