def main(args):
parser = get_parser()
arguments = parser.parse(args)
# Initialization
slices = ''
group_funcs = (('MEAN', func_wa), ('STD', func_std)) # functions to perform when aggregating metrics along S-I
fname_segmentation = sct.get_absolute_path(arguments['-i'])
fname_vert_levels = ''
if '-o' in arguments:
file_out = os.path.abspath(arguments['-o'])
else:
file_out = ''
if '-append' in arguments:
append = int(arguments['-append'])
else:
append = 0
if '-vert' in arguments:
vert_levels = arguments['-vert']
else:
vert_levels = ''
if '-r' in arguments:
remove_temp_files = arguments['-r']
if '-vertfile' in arguments:
fname_vert_levels = arguments['-vertfile']
if '-perlevel' in arguments:
perlevel = arguments['-perlevel']
else:
perlevel = None
if '-z' in arguments:
slices = arguments['-z']
if '-perslice' in arguments:
perslice = arguments['-perslice']
else:
perslice = None
if '-angle-corr' in arguments:
if arguments['-angle-corr'] == '1':
angle_correction = True
elif arguments['-angle-corr'] == '0':
angle_correction = False
param_centerline = ParamCenterline(
algo_fitting=arguments['-centerline-algo'],
smooth=arguments['-centerline-smooth'],
minmax=True)
path_qc = arguments.get("-qc", None)
qc_dataset = arguments.get("-qc-dataset", None)
qc_subject = arguments.get("-qc-subject", None)
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
# update fields
metrics_agg = {}
if not file_out:
file_out = 'csa.csv'
metrics, fit_results = process_seg.compute_shape(fname_segmentation,
angle_correction=angle_correction,
param_centerline=param_centerline,
verbose=verbose)
for key in metrics:
metrics_agg[key] = aggregate_per_slice_or_level(metrics[key], slices=parse_num_list(slices),
levels=parse_num_list(vert_levels), perslice=perslice,
perlevel=perlevel, vert_level=fname_vert_levels,
group_funcs=group_funcs)
metrics_agg_merged = _merge_dict(metrics_agg)
save_as_csv(metrics_agg_merged, file_out, fname_in=fname_segmentation, append=append)
# QC report (only show CSA for clarity)
if path_qc is not None:
generate_qc(fname_segmentation, args=args, path_qc=os.path.abspath(path_qc), dataset=qc_dataset,
subject=qc_subject, path_img=_make_figure(metrics_agg_merged, fit_results),
process='sct_process_segmentation')
sct.display_open(file_out)
def main(fname_data, path_label, method, slices, levels, fname_output, labels_user, append_csv,
fname_vertebral_labeling="", perslice=1, perlevel=1, verbose=1, combine_labels=True):
"""
Extract metrics from MRI data based on mask (could be single file of folder to atlas)
:param fname_data: data to extract metric from
:param path_label: mask: could be single file or folder to atlas (which contains info_label.txt)
:param method {'wa', 'bin', 'ml', 'map'}
:param slices. Slices of interest. Accepted format:
"0,1,2,3": slices 0,1,2,3
"0:3": slices 0,1,2,3
:param levels: Vertebral levels to extract metrics from. Should be associated with a template
(e.g. PAM50/template/) or a specified file: fname_vertebral_labeling. Same format as slices_of_interest.
:param fname_output:
:param labels_user:
:param append_csv: Append to csv file
:param fname_normalizing_label:
:param fname_vertebral_labeling: vertebral labeling to be used with vertebral_levels
:param perslice: if user selected several slices, then the function outputs a metric within each slice
instead of a single average output.
:param perlevel: if user selected several levels, then the function outputs a metric within each vertebral level
instead of a single average output.
:param verbose
:param combine_labels: bool: Combine labels into a single value
:return:
"""
# check if path_label is a file (e.g., single binary mask) instead of a folder (e.g., SCT atlas structure which
# contains info_label.txt file)
if os.path.isfile(path_label):
# Label is a single file
indiv_labels_ids = [0]
indiv_labels_files = [path_label]
combined_labels_ids = []
label_struc = {0: LabelStruc(id=0,
name=sct.extract_fname(path_label)[1],
filename=path_label)}
# set path_label to empty string, because indiv_labels_files will replace it from now on
path_label = ''
elif os.path.isdir(path_label):
# Labels is an SCT atlas folder structure
# Parse labels according to the file info_label.txt
# Note: the "combined_labels_*" is a list of single labels that are defined in the section defined by the keyword
# "# Keyword=CombinedLabels" in info_label.txt.
# TODO: redirect to appropriate Sphinx documentation
# TODO: output Class instead of multiple variables.
# Example 1:
# label_struc[2].id = (2)
# label_struc[2].name = "left fasciculus cuneatus"
# label_struc[2].filename = "PAM50_atlas_02.nii.gz"
# Example 2:
# label_struc[51].id = (1, 2, 3, ..., 29)
# label_struc[51].name = "White Matter"
# label_struc[51].filename = "" # no name because it is combined
indiv_labels_ids, indiv_labels_names, indiv_labels_files, \
combined_labels_ids, combined_labels_names, combined_labels_id_groups, map_clusters \
= read_label_file(path_label, param_default.file_info_label)
label_struc = {}
# fill IDs for indiv labels
for i_label in range(len(indiv_labels_ids)):
label_struc[indiv_labels_ids[i_label]] = LabelStruc(id=indiv_labels_ids[i_label],
name=indiv_labels_names[i_label],
filename=indiv_labels_files[i_label],
map_cluster=[indiv_labels_ids[i_label] in map_cluster for
map_cluster in map_clusters].index(True))
# fill IDs for combined labels
# TODO: problem for defining map_cluster: if labels overlap two regions, e.g. WM and GM (e.g. id=50),
# map_cluster will take value 0, which is wrong.
for i_label in range(len(combined_labels_ids)):
label_struc[combined_labels_ids[i_label]] = LabelStruc(id=combined_labels_id_groups[i_label],
name=combined_labels_names[i_label],
map_cluster=[indiv_labels_ids[i_label] in map_cluster for
map_cluster in map_clusters].index(True))
else:
raise RuntimeError(path_label + ' does not exist')
# check syntax of labels asked by user
labels_id_user = check_labels(indiv_labels_ids + combined_labels_ids, parse_num_list(labels_user))
nb_labels = len(indiv_labels_files)
# Load data and systematically reorient to RPI because we need the 3rd dimension to be z
sct.printv('\nLoad metric image...', verbose)
input_im = Image(fname_data).change_orientation("RPI")
data = Metric(data=input_im.data, label='')
# Load labels
labels_tmp = np.empty([nb_labels], dtype=object)
for i_label in range(nb_labels):
im_label = Image(os.path.join(path_label, indiv_labels_files[i_label])).change_orientation("RPI")
labels_tmp[i_label] = np.expand_dims(im_label.data, 3) # TODO: generalize to 2D input label
labels = np.concatenate(labels_tmp[:], 3) # labels: (x,y,z,label)
# Load vertebral levels
if vertebral_levels:
im_vertebral_labeling = Image(fname_vertebral_labeling).change_orientation("RPI")
else:
im_vertebral_labeling = None
# Get dimensions of data and labels
nx, ny, nz = data.data.shape
nx_atlas, ny_atlas, nz_atlas, nt_atlas = labels.shape
# Check dimensions consistency between atlas and data
if (nx, ny, nz) != (nx_atlas, ny_atlas, nz_atlas):
sct.printv('\nERROR: Metric data and labels DO NOT HAVE SAME DIMENSIONS.', 1, type='error')
# Combine individual labels for estimation
if combine_labels:
# Add entry with internal ID value (99) which corresponds to combined labels
label_struc[99] = LabelStruc(id=labels_id_user, name=','.join([str(i) for i in labels_id_user]),
map_cluster=None)
labels_id_user = [99]
for id_label in labels_id_user:
sct.printv('Estimation for label: '+label_struc[id_label].name, verbose)
agg_metric = extract_metric(data, labels=labels, slices=slices, levels=levels, perslice=perslice,
perlevel=perlevel, vert_level=im_vertebral_labeling, method=method,
label_struc=label_struc, id_label=id_label, indiv_labels_ids=indiv_labels_ids)
save_as_csv(agg_metric, fname_output, fname_in=fname_data, append=append_csv)
append_csv = True # when looping across labels, need to append results in the same file
sct.display_open(fname_output)
def main(args):
parser = get_parser()
arguments = parser.parse(args)
param = Param()
# Initialization
slices = param.slices
group_funcs = (('MEAN', func_wa), ('STD', func_std)
) # functions to perform when aggregating metrics along S-I
fname_segmentation = sct.get_absolute_path(arguments['-i'])
fname_vert_levels = ''
if '-o' in arguments:
file_out = os.path.abspath(arguments['-o'])
else:
file_out = ''
if '-append' in arguments:
append = int(arguments['-append'])
else:
append = 0
if '-vert' in arguments:
vert_levels = arguments['-vert']
else:
vert_levels = ''
if '-r' in arguments:
remove_temp_files = arguments['-r']
if '-vertfile' in arguments:
fname_vert_levels = arguments['-vertfile']
if '-perlevel' in arguments:
perlevel = arguments['-perlevel']
else:
perlevel = Param().perlevel
if '-z' in arguments:
slices = arguments['-z']
if '-perslice' in arguments:
perslice = arguments['-perslice']
else:
perslice = Param().perslice
if '-angle-corr' in arguments:
if arguments['-angle-corr'] == '1':
angle_correction = True
elif arguments['-angle-corr'] == '0':
angle_correction = False
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
# update fields
metrics_agg = {}
if not file_out:
file_out = 'csa.csv'
metrics = process_seg.compute_shape(fname_segmentation,
algo_fitting='bspline',
angle_correction=angle_correction,
verbose=verbose)
for key in metrics:
metrics_agg[key] = aggregate_per_slice_or_level(
metrics[key],
slices=parse_num_list(slices),
levels=parse_num_list(vert_levels),
perslice=perslice,
perlevel=perlevel,
vert_level=fname_vert_levels,
group_funcs=group_funcs)
metrics_agg_merged = _merge_dict(metrics_agg)
save_as_csv(metrics_agg_merged,
file_out,
fname_in=fname_segmentation,
append=append)
sct.display_open(file_out)
def main(args=None):
parser = get_parser()
if args:
arguments = parser.parse_args(args)
else:
arguments = parser.parse_args(
args=None if sys.argv[1:] else ['--help'])
# Initialization
slices = ''
group_funcs = (('MEAN', func_wa), ('STD', func_std)
) # functions to perform when aggregating metrics along S-I
fname_segmentation = sct.get_absolute_path(arguments.i)
fname_vert_levels = ''
if arguments.o is not None:
file_out = os.path.abspath(arguments.o)
else:
file_out = ''
if arguments.append is not None:
append = arguments.append
else:
append = 0
if arguments.vert is not None:
vert_levels = arguments.vert
else:
vert_levels = ''
remove_temp_files = arguments.r
if arguments.vertfile is not None:
fname_vert_levels = arguments.vertfile
if arguments.perlevel is not None:
perlevel = arguments.perlevel
else:
perlevel = None
if arguments.z is not None:
slices = arguments.z
if arguments.perslice is not None:
perslice = arguments.perslice
else:
perslice = None
angle_correction = arguments.angle_corr
param_centerline = ParamCenterline(algo_fitting=arguments.centerline_algo,
smooth=arguments.centerline_smooth,
minmax=True)
path_qc = arguments.qc
qc_dataset = arguments.qc_dataset
qc_subject = arguments.qc_subject
verbose = int(arguments.v)
init_sct(log_level=verbose, update=True) # Update log level
# update fields
metrics_agg = {}
if not file_out:
file_out = 'csa.csv'
metrics, fit_results = process_seg.compute_shape(
fname_segmentation,
angle_correction=angle_correction,
param_centerline=param_centerline,
verbose=verbose)
for key in metrics:
if key == 'length':
# For computing cord length, slice-wise length needs to be summed across slices
metrics_agg[key] = aggregate_per_slice_or_level(
metrics[key],
slices=parse_num_list(slices),
levels=parse_num_list(vert_levels),
perslice=perslice,
perlevel=perlevel,
vert_level=fname_vert_levels,
group_funcs=(('SUM', func_sum), ))
else:
# For other metrics, we compute the average and standard deviation across slices
metrics_agg[key] = aggregate_per_slice_or_level(
metrics[key],
slices=parse_num_list(slices),
levels=parse_num_list(vert_levels),
perslice=perslice,
perlevel=perlevel,
vert_level=fname_vert_levels,
group_funcs=group_funcs)
metrics_agg_merged = merge_dict(metrics_agg)
save_as_csv(metrics_agg_merged,
file_out,
fname_in=fname_segmentation,
append=append)
# QC report (only show CSA for clarity)
if path_qc is not None:
generate_qc(fname_segmentation,
args=args,
path_qc=os.path.abspath(path_qc),
dataset=qc_dataset,
subject=qc_subject,
path_img=_make_figure(metrics_agg_merged, fit_results),
process='sct_process_segmentation')
sct.display_open(file_out)
def main(fname_data, path_label, method, slices, levels, fname_output, labels_user, append,
fname_normalizing_label, normalization_method, label_to_fix, adv_param_user, fname_output_metric_map,
fname_mask_weight, fname_vertebral_labeling="", perslice=1, perlevel=1, discard_negative_values=False,
verbose=1):
"""
Extract metrics from MRI data based on mask (could be single file of folder to atlas)
:param fname_data: data to extract metric from
:param path_label: mask: could be single file or folder to atlas (which contains info_label.txt)
:param method:
:param slices_of_interest. Accepted format:
"0,1,2,3": slices 0,1,2,3
"0:3": slices 0,1,2,3
:param vertebral_levels: Vertebral levels to extract metrics from. Should be associated with a template
(e.g. PAM50/template/) or a specified file: fname_vertebral_labeling. Same format as slices_of_interest.
:param fname_output:
:param labels_user:
:param overwrite:
:param fname_normalizing_label:
:param normalization_method:
:param label_to_fix:
:param adv_param_user:
:param fname_output_metric_map:
:param fname_mask_weight:
:param fname_vertebral_labeling: vertebral labeling to be used with vertebral_levels
:param perslice: if user selected several slices, then the function outputs a metric within each slice
instead of a single average output.
:param perlevel: if user selected several levels, then the function outputs a metric within each vertebral level
instead of a single average output.
:param discard_negative_values: Bool: Discard negative voxels when computing metrics statistics
:param verbose
:return:
"""
# check if path_label is a file (e.g., single binary mask) instead of a folder (e.g., SCT atlas structure which
# contains info_label.txt file)
if os.path.isfile(path_label):
# Label is a single file
indiv_labels_ids = [0]
indiv_labels_names = [path_label]
indiv_labels_files = [path_label]
combined_labels_ids = []
combined_labels_names = []
combined_labels_id_groups = []
map_clusters = []
label_struc = {0: LabelStruc(id=0,
name=sct.extract_fname(path_label)[1],
filename=path_label)}
# set path_label to empty string, because indiv_labels_files will replace it from now on
path_label = ''
elif os.path.isdir(path_label):
# Labels is an SCT atlas folder structure
# Parse labels according to the file info_label.txt
# Note: the "combined_labels_*" is a list of single labels that are defined in the section defined by the keyword
# "# Keyword=CombinedLabels" in info_label.txt.
# TODO: redirect to appropriate Sphinx documentation
# TODO: output Class instead of multiple variables.
# Example 1:
# label_struc[2].id = (2)
# label_struc[2].name = "left fasciculus cuneatus"
# label_struc[2].filename = "PAM50_atlas_02.nii.gz"
# Example 2:
# label_struc[51].id = (1, 2, 3, ..., 29)
# label_struc[51].name = "White Matter"
# label_struc[51].filename = "" # no name because it is combined
indiv_labels_ids, indiv_labels_names, indiv_labels_files, \
combined_labels_ids, combined_labels_names, combined_labels_id_groups, map_clusters \
= read_label_file(path_label, param_default.file_info_label)
label_struc = {}
# fill IDs for indiv labels
for i_label in range(len(indiv_labels_ids)):
label_struc[indiv_labels_ids[i_label]] = LabelStruc(id=indiv_labels_ids[i_label],
name=indiv_labels_names[i_label],
filename=indiv_labels_files[i_label],
map_cluster=[indiv_labels_ids[i_label] in map_cluster for
map_cluster in map_clusters].index(True))
# fill IDs for combined labels
for i_label in range(len(combined_labels_ids)):
label_struc[combined_labels_ids[i_label]] = LabelStruc(id=combined_labels_id_groups[i_label],
name=combined_labels_names[i_label],
map_cluster=[indiv_labels_ids[i_label] in map_cluster for
map_cluster in map_clusters].index(True))
else:
sct.printv('\nERROR: ' + path_label + ' does not exist.', 1, 'error')
# check syntax of labels asked by user
labels_id_user = check_labels(indiv_labels_ids + combined_labels_ids, parse_num_list(labels_user))
nb_labels = len(indiv_labels_files)
# Load data and systematically reorient to RPI because we need the 3rd dimension to be z
sct.printv('\nLoad metric image...', verbose)
input_im = Image(fname_data).change_orientation("RPI")
data = Metric(data=input_im.data, label='')
# Load labels
labels_tmp = np.empty([nb_labels], dtype=object)
for i_label in range(nb_labels):
im_label = Image(os.path.join(path_label, indiv_labels_files[i_label])).change_orientation("RPI")
labels_tmp[i_label] = np.expand_dims(im_label.data, 3) # TODO: generalize to 2D input label
labels = np.concatenate(labels_tmp[:], 3) # labels: (x,y,z,label)
# Load vertebral levels
if vertebral_levels:
im_vertebral_labeling = Image(fname_vertebral_labeling).change_orientation("RPI")
else:
im_vertebral_labeling = None
# Get dimensions of data and labels
nx, ny, nz = data.data.shape
nx_atlas, ny_atlas, nz_atlas, nt_atlas = labels.shape
# Check dimensions consistency between atlas and data
if (nx, ny, nz) != (nx_atlas, ny_atlas, nz_atlas):
sct.printv('\nERROR: Metric data and labels DO NOT HAVE SAME DIMENSIONS.', 1, type='error')
for id_label in labels_id_user:
sct.printv('Estimation for label: '+label_struc[id_label].name, verbose)
agg_metric = extract_metric(data, labels=labels, slices=slices, levels=levels, perslice=perslice,
perlevel=perlevel, vert_level=im_vertebral_labeling, method=method,
label_struc=label_struc, id_label=id_label, indiv_labels_ids=indiv_labels_ids)
save_as_csv(agg_metric, fname_output, fname_in=fname_data, append=append)
append = True # when looping across labels, need to append results in the same file
sct.display_open(fname_output)
def main(args):
parser = get_parser()
arguments = parser.parse(args)
param = Param()
# Initialization
slices = param.slices
group_funcs = (('MEAN', func_wa), ('STD', func_std)) # functions to perform when aggregating metrics along S-I
fname_segmentation = sct.get_absolute_path(arguments['-i'])
fname_vert_levels = ''
if '-o' in arguments:
file_out = os.path.abspath(arguments['-o'])
else:
file_out = ''
if '-append' in arguments:
append = int(arguments['-append'])
else:
append = 0
if '-vert' in arguments:
vert_levels = arguments['-vert']
else:
vert_levels = ''
if '-r' in arguments:
remove_temp_files = arguments['-r']
if '-vertfile' in arguments:
fname_vert_levels = arguments['-vertfile']
if '-perlevel' in arguments:
perlevel = arguments['-perlevel']
else:
perlevel = Param().perlevel
if '-z' in arguments:
slices = arguments['-z']
if '-perslice' in arguments:
perslice = arguments['-perslice']
else:
perslice = Param().perslice
if '-angle-corr' in arguments:
if arguments['-angle-corr'] == '1':
angle_correction = True
elif arguments['-angle-corr'] == '0':
angle_correction = False
verbose = int(arguments.get('-v'))
sct.init_sct(log_level=verbose, update=True) # Update log level
# update fields
metrics_agg = {}
if not file_out:
file_out = 'csa.csv'
metrics = process_seg.compute_shape(fname_segmentation,
algo_fitting='bspline',
angle_correction=angle_correction,
verbose=verbose)
for key in metrics:
metrics_agg[key] = aggregate_per_slice_or_level(metrics[key], slices=parse_num_list(slices),
levels=parse_num_list(vert_levels), perslice=perslice,
perlevel=perlevel, vert_level=fname_vert_levels,
group_funcs=group_funcs)
metrics_agg_merged = _merge_dict(metrics_agg)
save_as_csv(metrics_agg_merged, file_out, fname_in=fname_segmentation, append=append)
sct.display_open(file_out)