def test_crop(fake_3dimage_sct): im_src = fake_3dimage_sct.copy() crop_spec = dict(((0, (1, 3)), (1, (2, 4)), (2, (3, 5)))) print(crop_spec) im_dst = msct_image.spatial_crop(im_src, crop_spec) print("Check shape") assert im_dst.data.shape == (3, 3, 3) print("Check world pos") aff_src = im_src.header.get_best_affine() aff_dst = im_dst.header.get_best_affine() pos_src = np.matmul(aff_src, np.array([[1, 2, 3, 1]]).T) pos_dst = np.matmul(aff_dst, np.array([[0, 0, 0, 1]]).T) assert (pos_src == pos_dst).all()
def test_crop(fake_3dimage_sct): im_src = fake_3dimage_sct.copy() crop_spec = dict(((0, (1,3)),(1, (2,4)),(2, (3,5)))) print(crop_spec) im_dst = msct_image.spatial_crop(im_src, crop_spec) print("Check shape") assert im_dst.data.shape == (3,3,3) print("Check world pos") aff_src = im_src.header.get_best_affine() aff_dst = im_dst.header.get_best_affine() pos_src = np.matmul(aff_src, np.array([[1,2,3,1]]).T) pos_dst = np.matmul(aff_dst, np.array([[0,0,0,1]]).T) assert (pos_src == pos_dst).all()
def register(src, dest, paramreg, param, i_step_str): # initiate default parameters of antsRegistration transformation ants_registration_params = { 'rigid': '', 'affine': '', 'compositeaffine': '', 'similarity': '', 'translation': '', 'bspline': ',10', 'gaussiandisplacementfield': ',3,0', 'bsplinedisplacementfield': ',5,10', 'syn': ',3,0', 'bsplinesyn': ',1,3' } output = '' # default output if problem # display arguments sct.printv('Registration parameters:', param.verbose) sct.printv(' type ........... ' + paramreg.steps[i_step_str].type, param.verbose) sct.printv(' algo ........... ' + paramreg.steps[i_step_str].algo, param.verbose) sct.printv(' slicewise ...... ' + paramreg.steps[i_step_str].slicewise, param.verbose) sct.printv(' metric ......... ' + paramreg.steps[i_step_str].metric, param.verbose) sct.printv(' iter ........... ' + paramreg.steps[i_step_str].iter, param.verbose) sct.printv(' smooth ......... ' + paramreg.steps[i_step_str].smooth, param.verbose) sct.printv(' laplacian ...... ' + paramreg.steps[i_step_str].laplacian, param.verbose) sct.printv(' shrink ......... ' + paramreg.steps[i_step_str].shrink, param.verbose) sct.printv(' gradStep ....... ' + paramreg.steps[i_step_str].gradStep, param.verbose) sct.printv(' deformation .... ' + paramreg.steps[i_step_str].deformation, param.verbose) sct.printv(' init ........... ' + paramreg.steps[i_step_str].init, param.verbose) sct.printv(' poly ........... ' + paramreg.steps[i_step_str].poly, param.verbose) sct.printv(' dof ............ ' + paramreg.steps[i_step_str].dof, param.verbose) sct.printv(' smoothWarpXY ... ' + paramreg.steps[i_step_str].smoothWarpXY, param.verbose) # set metricSize if paramreg.steps[i_step_str].metric == 'MI': metricSize = '32' # corresponds to number of bins else: metricSize = '4' # corresponds to radius (for CC, MeanSquares...) # set masking if param.fname_mask: fname_mask = 'mask.nii.gz' masking = ['-x', 'mask.nii.gz'] else: fname_mask = '' masking = [] if paramreg.steps[i_step_str].algo == 'slicereg': # check if user used type=label if paramreg.steps[i_step_str].type == 'label': sct.printv( '\nERROR: this algo is not compatible with type=label. Please use type=im or type=seg', 1, 'error') else: # Find the min (and max) z-slice index below which (and above which) slices only have voxels below a given # threshold. list_fname = [src, dest] if not masking == []: list_fname.append(fname_mask) zmin_global, zmax_global = 0, 99999 # this is assuming that typical image has less slice than 99999 for fname in list_fname: im = Image(fname) zmin, zmax = msct_image.find_zmin_zmax(im, threshold=0.1) if zmin > zmin_global: zmin_global = zmin if zmax < zmax_global: zmax_global = zmax # crop images (see issue #293) src_crop = sct.add_suffix(src, '_crop') msct_image.spatial_crop(Image(src), dict( ((2, (zmin_global, zmax_global)), ))).save(src_crop) dest_crop = sct.add_suffix(dest, '_crop') msct_image.spatial_crop(Image(dest), dict(((2, (zmin_global, zmax_global)), ))).save(dest_crop) # update variables src = src_crop dest = dest_crop scr_regStep = sct.add_suffix(src, '_regStep' + i_step_str) # estimate transfo # TODO fixup isct_ants* parsers cmd = [ 'isct_antsSliceRegularizedRegistration', '-t', 'Translation[' + paramreg.steps[i_step_str].gradStep + ']', '-m', paramreg.steps[i_step_str].metric + '[' + dest + ',' + src + ',1,' + metricSize + ',Regular,0.2]', '-p', paramreg.steps[i_step_str].poly, '-i', paramreg.steps[i_step_str].iter, '-f', paramreg.steps[i_step_str].shrink, '-s', paramreg.steps[i_step_str].smooth, '-v', '1', # verbose (verbose=2 does not exist, so we force it to 1) '-o', '[step' + i_step_str + ',' + scr_regStep + ']', # here the warp name is stage10 because # antsSliceReg add "Warp" ] + masking warp_forward_out = 'step' + i_step_str + 'Warp.nii.gz' warp_inverse_out = 'step' + i_step_str + 'InverseWarp.nii.gz' # run command status, output = sct.run(cmd, param.verbose) # ANTS 3d elif paramreg.steps[i_step_str].algo.lower() in ants_registration_params \ and paramreg.steps[i_step_str].slicewise == '0': # make sure type!=label. If type==label, this will be addressed later in the code. if not paramreg.steps[i_step_str].type == 'label': # Pad the destination image (because ants doesn't deform the extremities) # N.B. no need to pad if iter = 0 if not paramreg.steps[i_step_str].iter == '0': dest_pad = sct.add_suffix(dest, '_pad') sct.run([ 'sct_image', '-i', dest, '-o', dest_pad, '-pad', '0,0,' + str(param.padding) ]) dest = dest_pad # apply Laplacian filter if not paramreg.steps[i_step_str].laplacian == '0': sct.printv('\nApply Laplacian filter', param.verbose) sct.run([ 'sct_maths', '-i', src, '-laplacian', paramreg.steps[i_step_str].laplacian + ',' + paramreg.steps[i_step_str].laplacian + ',0', '-o', sct.add_suffix(src, '_laplacian') ]) sct.run([ 'sct_maths', '-i', dest, '-laplacian', paramreg.steps[i_step_str].laplacian + ',' + paramreg.steps[i_step_str].laplacian + ',0', '-o', sct.add_suffix(dest, '_laplacian') ]) src = sct.add_suffix(src, '_laplacian') dest = sct.add_suffix(dest, '_laplacian') # Estimate transformation sct.printv('\nEstimate transformation', param.verbose) scr_regStep = sct.add_suffix(src, '_regStep' + i_step_str) # TODO fixup isct_ants* parsers cmd = [ 'isct_antsRegistration', '--dimensionality', '3', '--transform', paramreg.steps[i_step_str].algo + '[' + paramreg.steps[i_step_str].gradStep + ants_registration_params[ paramreg.steps[i_step_str].algo.lower()] + ']', '--metric', paramreg.steps[i_step_str].metric + '[' + dest + ',' + src + ',1,' + metricSize + ']', '--convergence', paramreg.steps[i_step_str].iter, '--shrink-factors', paramreg.steps[i_step_str].shrink, '--smoothing-sigmas', paramreg.steps[i_step_str].smooth + 'mm', '--restrict-deformation', paramreg.steps[i_step_str].deformation, '--output', '[step' + i_step_str + ',' + scr_regStep + ']', '--interpolation', 'BSpline[3]', '--verbose', '1', ] + masking # add init translation if not paramreg.steps[i_step_str].init == '': init_dict = { 'geometric': '0', 'centermass': '1', 'origin': '2' } cmd += [ '-r', '[' + dest + ',' + src + ',' + init_dict[paramreg.steps[i_step_str].init] + ']' ] # run command status, output = sct.run(cmd, param.verbose) # get appropriate file name for transformation if paramreg.steps[i_step_str].algo in [ 'rigid', 'affine', 'translation' ]: warp_forward_out = 'step' + i_step_str + '0GenericAffine.mat' warp_inverse_out = '-step' + i_step_str + '0GenericAffine.mat' else: warp_forward_out = 'step' + i_step_str + '0Warp.nii.gz' warp_inverse_out = 'step' + i_step_str + '0InverseWarp.nii.gz' # ANTS 2d elif paramreg.steps[i_step_str].algo.lower() in ants_registration_params \ and paramreg.steps[i_step_str].slicewise == '1': # make sure type!=label. If type==label, this will be addressed later in the code. if not paramreg.steps[i_step_str].type == 'label': from msct_register import register_slicewise # if shrink!=1, force it to be 1 (otherwise, it generates a wrong 3d warping field). TODO: fix that! if not paramreg.steps[i_step_str].shrink == '1': sct.printv( '\nWARNING: when using slicewise with SyN or BSplineSyN, shrink factor needs to be one. ' 'Forcing shrink=1.', 1, 'warning') paramreg.steps[i_step_str].shrink = '1' warp_forward_out = 'step' + i_step_str + 'Warp.nii.gz' warp_inverse_out = 'step' + i_step_str + 'InverseWarp.nii.gz' register_slicewise( src, dest, paramreg=paramreg.steps[i_step_str], fname_mask=fname_mask, warp_forward_out=warp_forward_out, warp_inverse_out=warp_inverse_out, ants_registration_params=ants_registration_params, remove_temp_files=param.remove_temp_files, verbose=param.verbose) # slice-wise transfo elif paramreg.steps[i_step_str].algo in [ 'centermass', 'centermassrot', 'columnwise' ]: # if type=im, sends warning if paramreg.steps[i_step_str].type == 'im': sct.printv( '\nWARNING: algo ' + paramreg.steps[i_step_str].algo + ' should be used with type=seg.\n', 1, 'warning') # if type=label, exit with error elif paramreg.steps[i_step_str].type == 'label': sct.printv( '\nERROR: this algo is not compatible with type=label. Please use type=im or type=seg', 1, 'error') # check if user provided a mask-- if so, inform it will be ignored if not fname_mask == '': sct.printv( '\nWARNING: algo ' + paramreg.steps[i_step_str].algo + ' will ignore the provided mask.\n', 1, 'warning') # smooth data if not paramreg.steps[i_step_str].smooth == '0': sct.printv('\nSmooth data', param.verbose) sct.run([ 'sct_maths', '-i', src, '-smooth', paramreg.steps[i_step_str].smooth + ',' + paramreg.steps[i_step_str].smooth + ',0', '-o', sct.add_suffix(src, '_smooth') ]) sct.run([ 'sct_maths', '-i', dest, '-smooth', paramreg.steps[i_step_str].smooth + ',' + paramreg.steps[i_step_str].smooth + ',0', '-o', sct.add_suffix(dest, '_smooth') ]) src = sct.add_suffix(src, '_smooth') dest = sct.add_suffix(dest, '_smooth') from msct_register import register_slicewise warp_forward_out = 'step' + i_step_str + 'Warp.nii.gz' warp_inverse_out = 'step' + i_step_str + 'InverseWarp.nii.gz' register_slicewise(src, dest, paramreg=paramreg.steps[i_step_str], fname_mask=fname_mask, warp_forward_out=warp_forward_out, warp_inverse_out=warp_inverse_out, ants_registration_params=ants_registration_params, remove_temp_files=param.remove_temp_files, verbose=param.verbose) else: sct.printv( '\nERROR: algo ' + paramreg.steps[i_step_str].algo + ' does not exist. Exit program\n', 1, 'error') # landmark-based registration if paramreg.steps[i_step_str].type in ['label']: # check if user specified ilabel and dlabel # TODO warp_forward_out = 'step' + i_step_str + '0GenericAffine.txt' warp_inverse_out = '-step' + i_step_str + '0GenericAffine.txt' from msct_register_landmarks import register_landmarks register_landmarks(src, dest, paramreg.steps[i_step_str].dof, fname_affine=warp_forward_out, verbose=param.verbose) if not os.path.isfile(warp_forward_out): # no forward warping field for rigid and affine sct.printv( '\nERROR: file ' + warp_forward_out + ' doesn\'t exist (or is not a file).\n' + output + '\nERROR: ANTs failed. Exit program.\n', 1, 'error') elif not os.path.isfile(warp_inverse_out) and \ paramreg.steps[i_step_str].algo not in ['rigid', 'affine', 'translation'] and \ paramreg.steps[i_step_str].type not in ['label']: # no inverse warping field for rigid and affine sct.printv( '\nERROR: file ' + warp_inverse_out + ' doesn\'t exist (or is not a file).\n' + output + '\nERROR: ANTs failed. Exit program.\n', 1, 'error') else: # rename warping fields if (paramreg.steps[i_step_str].algo.lower() in ['rigid', 'affine', 'translation'] and paramreg.steps[i_step_str].slicewise == '0'): # if ANTs is used with affine/rigid --> outputs .mat file warp_forward = 'warp_forward_' + i_step_str + '.mat' os.rename(warp_forward_out, warp_forward) warp_inverse = '-warp_forward_' + i_step_str + '.mat' elif paramreg.steps[i_step_str].type in ['label']: # if label-based registration is used --> outputs .txt file warp_forward = 'warp_forward_' + i_step_str + '.txt' os.rename(warp_forward_out, warp_forward) warp_inverse = '-warp_forward_' + i_step_str + '.txt' else: warp_forward = 'warp_forward_' + i_step_str + '.nii.gz' warp_inverse = 'warp_inverse_' + i_step_str + '.nii.gz' os.rename(warp_forward_out, warp_forward) os.rename(warp_inverse_out, warp_inverse) return warp_forward, warp_inverse
def 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'] param.remove_temp_files = int(arguments.get('-r')) verbose = int(arguments.get('-v')) sct.init_sct(log_level=verbose, update=True) # Update log level param.verbose = verbose # TODO: not clean, unify verbose or param.verbose in code, but not both param_centerline = ParamCenterline( algo_fitting=arguments['-centerline-algo'], smooth=arguments['-centerline-smooth']) # 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(param.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, param_centerline) # 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_maths.main(['-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) resample_file(ftmp_data, add_suffix(ftmp_data, '_1mm'), '1.0x1.0x1.0', 'mm', 'linear', verbose) ftmp_data = add_suffix(ftmp_data, '_1mm') resample_file(ftmp_seg, add_suffix(ftmp_seg, '_1mm'), '1.0x1.0x1.0', 'mm', 'linear', verbose) 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_apply_transfo.main(args=[ '-i', ftmp_seg, '-w', 'warp_curve2straight.nii.gz', '-d', 'straight_ref.nii.gz', '-o', add_suffix(ftmp_seg, '_straight')]) else: from spinalcordtoolbox.straightening import SpinalCordStraightener sc_straight = SpinalCordStraightener(ftmp_seg, ftmp_seg) sc_straight.param_centerline = param_centerline sc_straight.output_filename = add_suffix(ftmp_seg, '_straight') sc_straight.path_output = './' sc_straight.qc = '0' sc_straight.remove_temp_files = param.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) sct_concat_transfo.main(args=[ '-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-reference', ftmp_label]) # Dilating the input label so they can be straighten without losing them sct.printv('\nDilating input labels using 3vox ball radius') sct_maths.main(['-i', ftmp_label, '-dilate', '3', '-o', add_suffix(ftmp_label, '_dilate')]) ftmp_label = add_suffix(ftmp_label, '_dilate') # Apply straightening to labels sct.printv('\nApply straightening to labels...', verbose) sct_apply_transfo.main(args=[ '-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) try: register_landmarks(ftmp_label, ftmp_template_label, paramreg.steps['0'].dof, fname_affine='straight2templateAffine.txt', verbose=verbose) except RuntimeError: raise('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') # Concatenate transformations: curve --> straight --> affine sct.printv('\nConcatenate transformations: curve --> straight --> affine...', verbose) sct_concat_transfo.main(args=[ '-w', ['warp_curve2straight.nii.gz', 'straight2templateAffine.txt'], '-d', 'template.nii', '-o', 'warp_curve2straightAffine.nii.gz']) # Apply transformation sct.printv('\nApply transformation...', verbose) sct_apply_transfo.main(args=[ '-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_apply_transfo.main(args=[ '-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 paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog': src_seg = ftmp_seg dest_seg = ftmp_template_seg # if step>1, apply warp_forward_concat to the src image to be used if i_step > 1: # apply transformation from previous step, to use as new src for registration sct_apply_transfo.main(args=[ '-i', src, '-d', dest, '-w', warp_forward, '-o', add_suffix(src, '_regStep' + str(i_step - 1)), '-x', interp_step]) src = add_suffix(src, '_regStep' + str(i_step - 1)) if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog': # also apply transformation to the seg sct_apply_transfo.main(args=[ '-i', src_seg, '-d', dest_seg, '-w', warp_forward, '-o', add_suffix(src, '_regStep' + str(i_step - 1)), '-x', interp_step]) src_seg = add_suffix(src_seg, '_regStep' + str(i_step - 1)) # register src --> dest # TODO: display param for debugging if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog': # im_seg case warp_forward_out, warp_inverse_out = register([src, src_seg], [dest, dest_seg], paramreg, param, str(i_step)) else: 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: anat --> template sct.printv('\nConcatenate transformations: anat --> template...', verbose) warp_forward.insert(0, 'warp_curve2straightAffine.nii.gz') sct_concat_transfo.main(args=[ '-w', warp_forward, '-d', 'template.nii', '-o', 'warp_anat2template.nii.gz']) # Concatenate transformations: template --> anat sct.printv('\nConcatenate transformations: template --> anat...', verbose) warp_inverse.reverse() if vertebral_alignment: warp_inverse.append('warp_straight2curve.nii.gz') sct_concat_transfo.main(args=[ '-w', warp_inverse, '-d', 'data.nii', '-o', 'warp_template2anat.nii.gz']) else: warp_inverse.append('straight2templateAffine.txt') warp_inverse.append('warp_straight2curve.nii.gz') sct_concat_transfo.main(args=[ '-w', warp_inverse, '-winv', ['straight2templateAffine.txt'], '-d', 'data.nii', '-o', 'warp_template2anat.nii.gz']) # 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-reference', 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) 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_apply_transfo.main(args=[ '-i', src, '-d', dest, '-w', warp_forward, '-o', add_suffix(src, '_regStep' + str(i_step - 1)), '-x', interp_step]) 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_concat_transfo.main(args=[ '-w', warp_forward, '-d', 'data.nii', '-o', 'warp_template2anat.nii.gz']) sct.printv('\nConcatenate transformations: subject --> template...', verbose) sct_concat_transfo.main(args=[ '-w', warp_inverse, '-winv', ['template2subjectAffine.txt'], '-d', 'template.nii', '-o', 'warp_anat2template.nii.gz']) # 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 param.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) qc_dataset = arguments.get("-qc-dataset", None) qc_subject = arguments.get("-qc-subject", None) 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), dataset=qc_dataset, subject=qc_subject, 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 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 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 algo_fitting = self.algo_fitting # 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, algo_fitting, self.degree, 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 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'] param.remove_temp_files = int(arguments.get('-r')) verbose = int(arguments.get('-v')) sct.init_sct(log_level=verbose, update=True) # Update log level param.verbose = verbose # TODO: not clean, unify verbose or param.verbose in code, but not both 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(param.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_maths.main(['-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) resample_file(ftmp_data, add_suffix(ftmp_data, '_1mm'), '1.0x1.0x1.0', 'mm', 'linear', verbose) ftmp_data = add_suffix(ftmp_data, '_1mm') resample_file(ftmp_seg, add_suffix(ftmp_seg, '_1mm'), '1.0x1.0x1.0', 'mm', 'linear', verbose) 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 spinalcordtoolbox.straightening 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 = param.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-reference', ftmp_label]) # Dilating the input label so they can be straighten without losing them sct.printv('\nDilating input labels using 3vox ball radius') sct_maths.main(['-i', ftmp_label, '-dilate', '3', '-o', add_suffix(ftmp_label, '_dilate')]) 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) try: register_landmarks(ftmp_label, ftmp_template_label, paramreg.steps['0'].dof, fname_affine='straight2templateAffine.txt', verbose=verbose) except RuntimeError: raise('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') # 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 paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog': src_seg = ftmp_seg dest_seg = ftmp_template_seg # if step>1, apply warp_forward_concat to the src image to be used if i_step > 1: # 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)) if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog': # also apply transformation to the seg sct.run(['sct_apply_transfo', '-i', src_seg, '-d', dest_seg, '-w', ','.join(warp_forward), '-o', add_suffix(src, '_regStep' + str(i_step - 1)), '-x', interp_step], verbose) src_seg = add_suffix(src_seg, '_regStep' + str(i_step - 1)) # register src --> dest # TODO: display param for debugging if paramreg.steps[str(i_step)].algo == 'centermassrot' and paramreg.steps[str(i_step)].rot_method == 'hog': # im_seg case warp_forward_out, warp_inverse_out = register([src, src_seg], [dest, dest_seg], paramreg, param, str(i_step)) else: 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-reference', 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) 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 param.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) qc_dataset = arguments.get("-qc-dataset", None) qc_subject = arguments.get("-qc-subject", None) 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), dataset=qc_dataset, subject=qc_subject, 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 register(src, dest, paramreg, param, i_step_str): # initiate default parameters of antsRegistration transformation ants_registration_params = {'rigid': '', 'affine': '', 'compositeaffine': '', 'similarity': '', 'translation': '', 'bspline': ',10', 'gaussiandisplacementfield': ',3,0', 'bsplinedisplacementfield': ',5,10', 'syn': ',3,0', 'bsplinesyn': ',1,3'} output = '' # default output if problem if paramreg.steps[i_step_str].algo == "centermassrot" and paramreg.steps[i_step_str].rot_method == 'hog': src_im = src[0] # user is expected to input images to src and dest dest_im = dest[0] src_seg = src[1] dest_seg = dest[1] del src del dest # to be sure it is not missused later # display arguments sct.printv('Registration parameters:', param.verbose) sct.printv(' type ........... ' + paramreg.steps[i_step_str].type, param.verbose) sct.printv(' algo ........... ' + paramreg.steps[i_step_str].algo, param.verbose) sct.printv(' slicewise ...... ' + paramreg.steps[i_step_str].slicewise, param.verbose) sct.printv(' metric ......... ' + paramreg.steps[i_step_str].metric, param.verbose) sct.printv(' iter ........... ' + paramreg.steps[i_step_str].iter, param.verbose) sct.printv(' smooth ......... ' + paramreg.steps[i_step_str].smooth, param.verbose) sct.printv(' laplacian ...... ' + paramreg.steps[i_step_str].laplacian, param.verbose) sct.printv(' shrink ......... ' + paramreg.steps[i_step_str].shrink, param.verbose) sct.printv(' gradStep ....... ' + paramreg.steps[i_step_str].gradStep, param.verbose) sct.printv(' deformation .... ' + paramreg.steps[i_step_str].deformation, param.verbose) sct.printv(' init ........... ' + paramreg.steps[i_step_str].init, param.verbose) sct.printv(' poly ........... ' + paramreg.steps[i_step_str].poly, param.verbose) sct.printv(' dof ............ ' + paramreg.steps[i_step_str].dof, param.verbose) sct.printv(' smoothWarpXY ... ' + paramreg.steps[i_step_str].smoothWarpXY, param.verbose) sct.printv(' rot_method ... ' + paramreg.steps[i_step_str].rot_method, param.verbose) # set metricSize if paramreg.steps[i_step_str].metric == 'MI': metricSize = '32' # corresponds to number of bins else: metricSize = '4' # corresponds to radius (for CC, MeanSquares...) # set masking if param.fname_mask: fname_mask = 'mask.nii.gz' masking = ['-x', 'mask.nii.gz'] else: fname_mask = '' masking = [] if paramreg.steps[i_step_str].algo == 'slicereg': # check if user used type=label if paramreg.steps[i_step_str].type == 'label': sct.printv('\nERROR: this algo is not compatible with type=label. Please use type=im or type=seg', 1, 'error') else: # Find the min (and max) z-slice index below which (and above which) slices only have voxels below a given # threshold. list_fname = [src, dest] if not masking == []: list_fname.append(fname_mask) zmin_global, zmax_global = 0, 99999 # this is assuming that typical image has less slice than 99999 for fname in list_fname: im = Image(fname) zmin, zmax = msct_image.find_zmin_zmax(im, threshold=0.1) if zmin > zmin_global: zmin_global = zmin if zmax < zmax_global: zmax_global = zmax # crop images (see issue #293) src_crop = sct.add_suffix(src, '_crop') msct_image.spatial_crop(Image(src), dict(((2, (zmin_global, zmax_global)),))).save(src_crop) dest_crop = sct.add_suffix(dest, '_crop') msct_image.spatial_crop(Image(dest), dict(((2, (zmin_global, zmax_global)),))).save(dest_crop) # update variables src = src_crop dest = dest_crop scr_regStep = sct.add_suffix(src, '_regStep' + i_step_str) # estimate transfo # TODO fixup isct_ants* parsers cmd = ['isct_antsSliceRegularizedRegistration', '-t', 'Translation[' + paramreg.steps[i_step_str].gradStep + ']', '-m', paramreg.steps[i_step_str].metric + '[' + dest + ',' + src + ',1,' + metricSize + ',Regular,0.2]', '-p', paramreg.steps[i_step_str].poly, '-i', paramreg.steps[i_step_str].iter, '-f', paramreg.steps[i_step_str].shrink, '-s', paramreg.steps[i_step_str].smooth, '-v', '1', # verbose (verbose=2 does not exist, so we force it to 1) '-o', '[step' + i_step_str + ',' + scr_regStep + ']', # here the warp name is stage10 because # antsSliceReg add "Warp" ] + masking warp_forward_out = 'step' + i_step_str + 'Warp.nii.gz' warp_inverse_out = 'step' + i_step_str + 'InverseWarp.nii.gz' # run command status, output = sct.run(cmd, param.verbose, is_sct_binary=True) # ANTS 3d elif paramreg.steps[i_step_str].algo.lower() in ants_registration_params \ and paramreg.steps[i_step_str].slicewise == '0': # make sure type!=label. If type==label, this will be addressed later in the code. if not paramreg.steps[i_step_str].type == 'label': # Pad the destination image (because ants doesn't deform the extremities) # N.B. no need to pad if iter = 0 if not paramreg.steps[i_step_str].iter == '0': dest_pad = sct.add_suffix(dest, '_pad') sct.run(['sct_image', '-i', dest, '-o', dest_pad, '-pad', '0,0,' + str(param.padding)]) dest = dest_pad # apply Laplacian filter if not paramreg.steps[i_step_str].laplacian == '0': sct.printv('\nApply Laplacian filter', param.verbose) sct.run(['sct_maths', '-i', src, '-laplacian', paramreg.steps[i_step_str].laplacian + ',' + paramreg.steps[i_step_str].laplacian + ',0', '-o', sct.add_suffix(src, '_laplacian')]) sct.run(['sct_maths', '-i', dest, '-laplacian', paramreg.steps[i_step_str].laplacian + ',' + paramreg.steps[i_step_str].laplacian + ',0', '-o', sct.add_suffix(dest, '_laplacian')]) src = sct.add_suffix(src, '_laplacian') dest = sct.add_suffix(dest, '_laplacian') # Estimate transformation sct.printv('\nEstimate transformation', param.verbose) scr_regStep = sct.add_suffix(src, '_regStep' + i_step_str) # TODO fixup isct_ants* parsers cmd = ['isct_antsRegistration', '--dimensionality', '3', '--transform', paramreg.steps[i_step_str].algo + '[' + paramreg.steps[i_step_str].gradStep + ants_registration_params[paramreg.steps[i_step_str].algo.lower()] + ']', '--metric', paramreg.steps[i_step_str].metric + '[' + dest + ',' + src + ',1,' + metricSize + ']', '--convergence', paramreg.steps[i_step_str].iter, '--shrink-factors', paramreg.steps[i_step_str].shrink, '--smoothing-sigmas', paramreg.steps[i_step_str].smooth + 'mm', '--restrict-deformation', paramreg.steps[i_step_str].deformation, '--output', '[step' + i_step_str + ',' + scr_regStep + ']', '--interpolation', 'BSpline[3]', '--verbose', '1', ] + masking # add init translation if not paramreg.steps[i_step_str].init == '': init_dict = {'geometric': '0', 'centermass': '1', 'origin': '2'} cmd += ['-r', '[' + dest + ',' + src + ',' + init_dict[paramreg.steps[i_step_str].init] + ']'] # run command status, output = sct.run(cmd, param.verbose, is_sct_binary=True) # get appropriate file name for transformation if paramreg.steps[i_step_str].algo in ['rigid', 'affine', 'translation']: warp_forward_out = 'step' + i_step_str + '0GenericAffine.mat' warp_inverse_out = '-step' + i_step_str + '0GenericAffine.mat' else: warp_forward_out = 'step' + i_step_str + '0Warp.nii.gz' warp_inverse_out = 'step' + i_step_str + '0InverseWarp.nii.gz' # ANTS 2d elif paramreg.steps[i_step_str].algo.lower() in ants_registration_params \ and paramreg.steps[i_step_str].slicewise == '1': # make sure type!=label. If type==label, this will be addressed later in the code. if not paramreg.steps[i_step_str].type == 'label': from msct_register import register_slicewise # if shrink!=1, force it to be 1 (otherwise, it generates a wrong 3d warping field). TODO: fix that! if not paramreg.steps[i_step_str].shrink == '1': sct.printv('\nWARNING: when using slicewise with SyN or BSplineSyN, shrink factor needs to be one. ' 'Forcing shrink=1.', 1, 'warning') paramreg.steps[i_step_str].shrink = '1' warp_forward_out = 'step' + i_step_str + 'Warp.nii.gz' warp_inverse_out = 'step' + i_step_str + 'InverseWarp.nii.gz' register_slicewise(src, dest, paramreg=paramreg.steps[i_step_str], fname_mask=fname_mask, warp_forward_out=warp_forward_out, warp_inverse_out=warp_inverse_out, ants_registration_params=ants_registration_params, remove_temp_files=param.remove_temp_files, verbose=param.verbose) # slice-wise transfo elif paramreg.steps[i_step_str].algo in ['centermass', 'centermassrot', 'columnwise']: # if type=label, exit with error if paramreg.steps[i_step_str].type == 'label': sct.printv('\nERROR: this algo is not compatible with type=label. Please use type=im or type=seg', 1, 'error') # check if user provided a mask-- if so, inform it will be ignored if not fname_mask == '': sct.printv('\nWARNING: algo ' + paramreg.steps[i_step_str].algo + ' will ignore the provided mask.\n', 1, 'warning') # smooth data if not paramreg.steps[i_step_str].smooth == '0': sct.printv('\nSmooth data', param.verbose) if paramreg.steps[i_step_str].rot_method == 'pca': sct.run(['sct_maths', '-i', src, '-smooth', paramreg.steps[i_step_str].smooth + ',' + paramreg.steps[i_step_str].smooth + ',0', '-o', sct.add_suffix(src, '_smooth')]) sct.run(['sct_maths', '-i', dest, '-smooth', paramreg.steps[i_step_str].smooth + ',' + paramreg.steps[i_step_str].smooth + ',0', '-o', sct.add_suffix(dest, '_smooth')]) src = sct.add_suffix(src, '_smooth') dest = sct.add_suffix(dest, '_smooth') else: sct.run(['sct_maths', '-i', src_im, '-smooth', paramreg.steps[i_step_str].smooth + ',' + paramreg.steps[i_step_str].smooth + ',0', '-o', sct.add_suffix(src_im, '_smooth')]) sct.run(['sct_maths', '-i', src_seg, '-smooth', paramreg.steps[i_step_str].smooth + ',' + paramreg.steps[i_step_str].smooth + ',0', '-o', sct.add_suffix(src_seg, '_smooth')]) sct.run(['sct_maths', '-i', dest_im, '-smooth', paramreg.steps[i_step_str].smooth + ',' + paramreg.steps[i_step_str].smooth + ',0', '-o', sct.add_suffix(dest_im, '_smooth')]) sct.run(['sct_maths', '-i', dest_seg, '-smooth', paramreg.steps[i_step_str].smooth + ',' + paramreg.steps[i_step_str].smooth + ',0', '-o', sct.add_suffix(dest_seg, '_smooth')]) src_im = sct.add_suffix(src_im, '_smooth') dest_im = sct.add_suffix(dest_im, '_smooth') src_seg = sct.add_suffix(src_seg, '_smooth') dest_seg = sct.add_suffix(dest_seg, '_smooth') from msct_register import register_slicewise warp_forward_out = 'step' + i_step_str + 'Warp.nii.gz' warp_inverse_out = 'step' + i_step_str + 'InverseWarp.nii.gz' if paramreg.steps[i_step_str].rot_method == 'pca': #because pca is the default choice, also includes no rotation register_slicewise(src, dest, paramreg=paramreg.steps[i_step_str], fname_mask=fname_mask, warp_forward_out=warp_forward_out, warp_inverse_out=warp_inverse_out, ants_registration_params=ants_registration_params, remove_temp_files=param.remove_temp_files, verbose=param.verbose) elif paramreg.steps[i_step_str].rot_method == 'hog': # im_seg case register_slicewise([src_im, src_seg], [dest_im, dest_seg], paramreg=paramreg.steps[i_step_str], fname_mask=fname_mask, warp_forward_out=warp_forward_out, warp_inverse_out=warp_inverse_out, ants_registration_params=ants_registration_params, path_qc=param.path_qc, remove_temp_files=param.remove_temp_files, verbose=param.verbose) else: raise ValueError("rot_method " + paramreg.steps[i_step_str].rot_method + " does not exist") else: sct.printv('\nERROR: algo ' + paramreg.steps[i_step_str].algo + ' does not exist. Exit program\n', 1, 'error') # landmark-based registration if paramreg.steps[i_step_str].type in ['label']: # check if user specified ilabel and dlabel # TODO warp_forward_out = 'step' + i_step_str + '0GenericAffine.txt' warp_inverse_out = '-step' + i_step_str + '0GenericAffine.txt' from msct_register_landmarks import register_landmarks register_landmarks(src, dest, paramreg.steps[i_step_str].dof, fname_affine=warp_forward_out, verbose=param.verbose) if not os.path.isfile(warp_forward_out): # no forward warping field for rigid and affine sct.printv('\nERROR: file ' + warp_forward_out + ' doesn\'t exist (or is not a file).\n' + output + '\nERROR: ANTs failed. Exit program.\n', 1, 'error') elif not os.path.isfile(warp_inverse_out) and \ paramreg.steps[i_step_str].algo not in ['rigid', 'affine', 'translation'] and \ paramreg.steps[i_step_str].type not in ['label']: # no inverse warping field for rigid and affine sct.printv('\nERROR: file ' + warp_inverse_out + ' doesn\'t exist (or is not a file).\n' + output + '\nERROR: ANTs failed. Exit program.\n', 1, 'error') else: # rename warping fields if (paramreg.steps[i_step_str].algo.lower() in ['rigid', 'affine', 'translation'] and paramreg.steps[i_step_str].slicewise == '0'): # if ANTs is used with affine/rigid --> outputs .mat file warp_forward = 'warp_forward_' + i_step_str + '.mat' os.rename(warp_forward_out, warp_forward) warp_inverse = '-warp_forward_' + i_step_str + '.mat' elif paramreg.steps[i_step_str].type in ['label']: # if label-based registration is used --> outputs .txt file warp_forward = 'warp_forward_' + i_step_str + '.txt' os.rename(warp_forward_out, warp_forward) warp_inverse = '-warp_forward_' + i_step_str + '.txt' else: warp_forward = 'warp_forward_' + i_step_str + '.nii.gz' warp_inverse = 'warp_inverse_' + i_step_str + '.nii.gz' os.rename(warp_forward_out, warp_forward) os.rename(warp_inverse_out, warp_inverse) return warp_forward, warp_inverse