def load_manual_gmseg(list_slices_target, list_fname_manual_gmseg, tmp_dir, im_sc_seg_rpi, new_res, square_size_size_mm, for_model=False, fname_mask=None): if isinstance(list_fname_manual_gmseg, str): # consider fname_manual_gmseg as a list of file names to allow multiple manual GM segmentation list_fname_manual_gmseg = [list_fname_manual_gmseg] curdir = os.getcwd() for fname_manual_gmseg in list_fname_manual_gmseg: sct.copy(fname_manual_gmseg, tmp_dir) # change fname level to only file name (path = tmp dir now) path_gm, file_gm, ext_gm = extract_fname(fname_manual_gmseg) fname_manual_gmseg = file_gm + ext_gm os.chdir(tmp_dir) im_manual_gmseg = Image(fname_manual_gmseg).change_orientation("RPI") if fname_mask is not None: fname_gmseg_crop = add_suffix(im_manual_gmseg.absolutepath, '_pre_crop') crop_im = ImageCropper(input_file=im_manual_gmseg.absolutepath, output_file=fname_gmseg_crop, mask=fname_mask) im_manual_gmseg_crop = crop_im.crop() im_manual_gmseg = im_manual_gmseg_crop # assert gmseg has the right number of slices assert im_manual_gmseg.data.shape[2] == len(list_slices_target), 'ERROR: the manual GM segmentation has not the same number of slices than the image.' # interpolate gm to reference image nz_gmseg, nx_gmseg, ny_gmseg, nt_gmseg, pz_gmseg, px_gmseg, py_gmseg, pt_gmseg = im_manual_gmseg.dim list_im_gm = interpolate_im_to_ref(im_manual_gmseg, im_sc_seg_rpi, new_res=new_res, sq_size_size_mm=square_size_size_mm, interpolation_mode=0) # load gm seg in list of slices n_poped = 0 for im_gm, slice_im in zip(list_im_gm, list_slices_target): if im_gm.data.max() == 0 and for_model: list_slices_target.pop(slice_im.id - n_poped) n_poped += 1 else: slice_im.gm_seg.append(im_gm.data) wm_slice = (slice_im.im > 0) - im_gm.data slice_im.wm_seg.append(wm_slice) os.chdir(curdir) return list_slices_target
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 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 i in range(len(fname_warp_list)): # Check if inverse matrix is specified with '-' at the beginning of file name if fname_warp_list[i].find('-') == 0: use_inverse.append('-i ') fname_warp_list[i] = fname_warp_list[i][1:] # remove '-' else: use_inverse.append('') sct.printv( ' Transfo #' + str(i) + ': ' + use_inverse[i] + fname_warp_list[i], verbose) fname_warp_list_invert.append(use_inverse[i] + fname_warp_list[i]) # 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]) 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() # 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 = path_out + file_out + ext_out # Get dimensions of data sct.printv('\nGet dimensions of data...', verbose) from msct_image import Image nx, ny, nz, nt, px, py, pz, pt = Image(fname_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 ' + ' '.join(fname_warp_list_invert) + ' -r ' + fname_dest + interp, verbose) # if 4d, loop across the T dimension else: # create temporary folder sct.printv('\nCreate temporary folder...', verbose) path_tmp = sct.slash_at_the_end( 'tmp.' + time.strftime("%y%m%d%H%M%S"), 1) # sct.run('mkdir '+path_tmp, verbose) sct.run('mkdir ' + path_tmp, verbose) # convert to nifti into temp folder sct.printv( '\nCopying input data to tmp folder and convert to nii...', verbose) from sct_convert import convert convert(fname_src, path_tmp + 'data.nii', squeeze_data=False) sct.run('cp ' + fname_dest + ' ' + 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.run('cp ' + fname_warp + ' ' + 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] os.chdir(path_tmp) # split along T dimension sct.printv('\nSplit along T dimension...', verbose) from sct_image import split_data im_dat = Image('data.nii') im_header = im_dat.hdr data_split_list = 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 ' + ' '.join(fname_warp_list_invert_tmp) + ' -r ' + file_dest + ext_dest + interp, verbose) # Merge files back sct.printv('\nMerge file back...', verbose) from sct_image import concat_data 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') im_out = concat_data(fname_list, 3, im_header['pixdim']) im_out.setFileName(name_out + ext_out) im_out.save(squeeze_data=False) os.chdir('..') sct.generate_output_file(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.run('rm -rf ' + path_tmp, verbose, error_exit='warning') # 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) # display elapsed time sct.printv('\nDone! To view results, type:', verbose) sct.printv('fslview ' + fname_dest + ' ' + fname_out + ' &\n', verbose, 'info')
def load_manual_gmseg(list_slices_target, list_fname_manual_gmseg, tmp_dir, im_sc_seg_rpi, new_res, square_size_size_mm, for_model=False, fname_mask=None): if isinstance(list_fname_manual_gmseg, str): # consider fname_manual_gmseg as a list of file names to allow multiple manual GM segmentation list_fname_manual_gmseg = [list_fname_manual_gmseg] curdir = os.getcwd() for fname_manual_gmseg in list_fname_manual_gmseg: sct.copy(fname_manual_gmseg, tmp_dir) # change fname level to only file name (path = tmp dir now) path_gm, file_gm, ext_gm = extract_fname(fname_manual_gmseg) fname_manual_gmseg = file_gm + ext_gm os.chdir(tmp_dir) im_manual_gmseg = Image(fname_manual_gmseg) # reorient to RPI im_manual_gmseg = set_orientation(im_manual_gmseg, 'RPI') if fname_mask is not None: fname_gmseg_crop = add_suffix(im_manual_gmseg.absolutepath, '_pre_crop') crop_im = ImageCropper(input_file=im_manual_gmseg.absolutepath, output_file=fname_gmseg_crop, mask=fname_mask) im_manual_gmseg_crop = crop_im.crop() im_manual_gmseg = im_manual_gmseg_crop # assert gmseg has the right number of slices assert im_manual_gmseg.data.shape[2] == len( list_slices_target ), 'ERROR: the manual GM segmentation has not the same number of slices than the image.' # interpolate gm to reference image nz_gmseg, nx_gmseg, ny_gmseg, nt_gmseg, pz_gmseg, px_gmseg, py_gmseg, pt_gmseg = im_manual_gmseg.dim list_im_gm = interpolate_im_to_ref(im_manual_gmseg, im_sc_seg_rpi, new_res=new_res, sq_size_size_mm=square_size_size_mm, interpolation_mode=0) # load gm seg in list of slices n_poped = 0 for im_gm, slice_im in zip(list_im_gm, list_slices_target): if im_gm.data.max() == 0 and for_model: list_slices_target.pop(slice_im.id - n_poped) n_poped += 1 else: slice_im.gm_seg.append(im_gm.data) wm_slice = (slice_im.im > 0) - im_gm.data slice_im.wm_seg.append(wm_slice) os.chdir(curdir) return list_slices_target
def pre_processing(fname_target, fname_sc_seg, fname_level=None, fname_manual_gmseg=None, new_res=0.3, square_size_size_mm=22.5, denoising=True, verbose=1, rm_tmp=True, for_model=False): printv('\nPre-process data...', verbose, 'normal') tmp_dir = sct.tmp_create() sct.copy(fname_target, tmp_dir) fname_target = ''.join(extract_fname(fname_target)[1:]) sct.copy(fname_sc_seg, tmp_dir) fname_sc_seg = ''.join(extract_fname(fname_sc_seg)[1:]) curdir = os.getcwd() os.chdir(tmp_dir) original_info = { 'orientation': None, 'im_sc_seg_rpi': None, 'interpolated_images': [] } im_target = Image(fname_target).copy() im_sc_seg = Image(fname_sc_seg).copy() # get original orientation printv(' Reorient...', verbose, 'normal') original_info['orientation'] = im_target.orientation # assert images are in the same orientation assert im_target.orientation == im_sc_seg.orientation, "ERROR: the image to segment and it's SC segmentation are not in the same orientation" im_target_rpi = set_orientation(im_target, 'RPI') im_sc_seg_rpi = set_orientation(im_sc_seg, 'RPI') original_info['im_sc_seg_rpi'] = im_sc_seg_rpi.copy( ) # target image in RPI will be used to post-process segmentations # denoise using P. Coupe non local means algorithm (see [Manjon et al. JMRI 2010]) implemented in dipy if denoising: printv(' Denoise...', verbose, 'normal') # crop image before denoising to fasten denoising nx, ny, nz, nt, px, py, pz, pt = im_target_rpi.dim size_x, size_y = (square_size_size_mm + 1) / px, (square_size_size_mm + 1) / py size = int(math.ceil(max(size_x, size_y))) # create mask fname_mask = 'mask_pre_crop.nii.gz' sct_create_mask.main([ '-i', im_target_rpi.absolutepath, '-p', 'centerline,' + im_sc_seg_rpi.absolutepath, '-f', 'box', '-size', str(size), '-o', fname_mask ]) # crop image fname_target_crop = add_suffix(im_target_rpi.absolutepath, '_pre_crop') crop_im = ImageCropper(input_file=im_target_rpi.absolutepath, output_file=fname_target_crop, mask=fname_mask) im_target_rpi_crop = crop_im.crop() # crop segmentation fname_sc_seg_crop = add_suffix(im_sc_seg_rpi.absolutepath, '_pre_crop') crop_sc_seg = ImageCropper(input_file=im_sc_seg_rpi.absolutepath, output_file=fname_sc_seg_crop, mask=fname_mask) im_sc_seg_rpi_crop = crop_sc_seg.crop() # denoising from sct_maths import denoise_nlmeans block_radius = 3 block_radius = int( im_target_rpi_crop.data.shape[2] / 2) if im_target_rpi_crop.data.shape[2] < (block_radius * 2) else block_radius patch_radius = block_radius - 1 data_denoised = denoise_nlmeans(im_target_rpi_crop.data, block_radius=block_radius, patch_radius=patch_radius) im_target_rpi_crop.data = data_denoised im_target_rpi = im_target_rpi_crop im_sc_seg_rpi = im_sc_seg_rpi_crop else: fname_mask = None # interpolate image to reference square image (resample and square crop centered on SC) printv(' Interpolate data to the model space...', verbose, 'normal') list_im_slices = interpolate_im_to_ref(im_target_rpi, im_sc_seg_rpi, new_res=new_res, sq_size_size_mm=square_size_size_mm) original_info[ 'interpolated_images'] = list_im_slices # list of images (not Slice() objects) printv(' Mask data using the spinal cord segmentation...', verbose, 'normal') list_sc_seg_slices = interpolate_im_to_ref( im_sc_seg_rpi, im_sc_seg_rpi, new_res=new_res, sq_size_size_mm=square_size_size_mm, interpolation_mode=1) for i in range(len(list_im_slices)): # list_im_slices[i].data[list_sc_seg_slices[i].data == 0] = 0 list_sc_seg_slices[i] = binarize(list_sc_seg_slices[i], thr_min=0.5, thr_max=1) list_im_slices[ i].data = list_im_slices[i].data * list_sc_seg_slices[i].data printv(' Split along rostro-caudal direction...', verbose, 'normal') list_slices_target = [ Slice(slice_id=i, im=im_slice.data, gm_seg=[], wm_seg=[]) for i, im_slice in enumerate(list_im_slices) ] # load vertebral levels if fname_level is not None: printv(' Load vertebral levels...', verbose, 'normal') # copy level file to tmp dir os.chdir(curdir) sct.copy(fname_level, tmp_dir) os.chdir(tmp_dir) # change fname level to only file name (path = tmp dir now) fname_level = ''.join(extract_fname(fname_level)[1:]) # load levels list_slices_target = load_level(list_slices_target, fname_level) os.chdir(curdir) # load manual gmseg if there is one (model data) if fname_manual_gmseg is not None: printv('\n\tLoad manual GM segmentation(s) ...', verbose, 'normal') list_slices_target = load_manual_gmseg(list_slices_target, fname_manual_gmseg, tmp_dir, im_sc_seg_rpi, new_res, square_size_size_mm, for_model=for_model, fname_mask=fname_mask) if rm_tmp: # remove tmp folder sct.rmtree(tmp_dir) return list_slices_target, original_info
def 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 fname_target_crop = add_suffix(im_target_rpi.absolutepath, '_pre_crop') crop_im = ImageCropper(input_file=im_target_rpi.absolutepath, output_file=fname_target_crop, mask=fname_mask) im_target_rpi_crop = crop_im.crop() # crop segmentation fname_sc_seg_crop = add_suffix(im_sc_seg_rpi.absolutepath, '_pre_crop') crop_sc_seg = ImageCropper(input_file=im_sc_seg_rpi.absolutepath, output_file=fname_sc_seg_crop, mask=fname_mask) im_sc_seg_rpi_crop = crop_sc_seg.crop() # denoising from sct_maths import denoise_nlmeans block_radius = 3 block_radius = int(im_target_rpi_crop.data.shape[2] / 2) if im_target_rpi_crop.data.shape[2] < (block_radius*2) else block_radius patch_radius = block_radius -1 data_denoised = denoise_nlmeans(im_target_rpi_crop.data, block_radius=block_radius, patch_radius=patch_radius) im_target_rpi_crop.data = data_denoised im_target_rpi = im_target_rpi_crop im_sc_seg_rpi = im_sc_seg_rpi_crop else: fname_mask = None # interpolate image to reference square image (resample and square crop centered on SC) printv(' Interpolate data to the model space...', verbose, 'normal') list_im_slices = interpolate_im_to_ref(im_target_rpi, im_sc_seg_rpi, new_res=new_res, sq_size_size_mm=square_size_size_mm) original_info['interpolated_images'] = list_im_slices # list of images (not Slice() objects) printv(' Mask data using the spinal cord segmentation...', verbose, 'normal') list_sc_seg_slices = interpolate_im_to_ref(im_sc_seg_rpi, im_sc_seg_rpi, new_res=new_res, sq_size_size_mm=square_size_size_mm, interpolation_mode=1) for i in range(len(list_im_slices)): # list_im_slices[i].data[list_sc_seg_slices[i].data == 0] = 0 list_sc_seg_slices[i] = binarize(list_sc_seg_slices[i], thr_min=0.5, thr_max=1) list_im_slices[i].data = list_im_slices[i].data * list_sc_seg_slices[i].data printv(' Split along rostro-caudal direction...', verbose, 'normal') list_slices_target = [Slice(slice_id=i, im=im_slice.data, gm_seg=[], wm_seg=[]) for i, im_slice in enumerate(list_im_slices)] # load vertebral levels if fname_level is not None: printv(' Load vertebral levels...', verbose, 'normal') # copy level file to tmp dir os.chdir(curdir) sct.copy(fname_level, tmp_dir) os.chdir(tmp_dir) # change fname level to only file name (path = tmp dir now) fname_level = ''.join(extract_fname(fname_level)[1:]) # load levels list_slices_target = load_level(list_slices_target, fname_level) os.chdir(curdir) # load manual gmseg if there is one (model data) if fname_manual_gmseg is not None: printv('\n\tLoad manual GM segmentation(s) ...', verbose, 'normal') list_slices_target = load_manual_gmseg(list_slices_target, fname_manual_gmseg, tmp_dir, im_sc_seg_rpi, new_res, square_size_size_mm, for_model=for_model, fname_mask=fname_mask) if rm_tmp: # remove tmp folder sct.rmtree(tmp_dir) return list_slices_target, original_info