def test_extract_metric(dummy_data_and_labels):
# TODO: test with combined labels
"""Test different estimation methods."""
# Weighted average
agg_metric = aggregate_slicewise.extract_metric(dummy_data_and_labels[0], labels=dummy_data_and_labels[1],
label_struc=dummy_data_and_labels[2], id_label=0,
perslice=False, method='wa')
assert agg_metric[list(agg_metric)[0]]['WA()'] == 38.0
# Binarized mask
agg_metric = aggregate_slicewise.extract_metric(dummy_data_and_labels[0], labels=dummy_data_and_labels[1],
label_struc=dummy_data_and_labels[2], id_label=0,
perslice=False, method='bin')
assert agg_metric[list(agg_metric)[0]]['BIN()'] == pytest.approx(36.66, rel=0.01)
# Maximum Likelihood
agg_metric = aggregate_slicewise.extract_metric(dummy_data_and_labels[0], labels=dummy_data_and_labels[1],
label_struc=dummy_data_and_labels[2], id_label=0,
indiv_labels_ids=[0, 1, 2], perslice=False, method='ml')
assert agg_metric[list(agg_metric)[0]]['ML()'] == pytest.approx(39.9, rel=0.01)
# Maximum A Posteriori
agg_metric = aggregate_slicewise.extract_metric(dummy_data_and_labels[0], labels=dummy_data_and_labels[1],
label_struc=dummy_data_and_labels[2], id_label=0,
indiv_labels_ids=[0, 1, 2], perslice=False, method='map')
assert agg_metric[list(agg_metric)[0]]['MAP()'] == pytest.approx(40.0, rel=0.01)
# Maximum
agg_metric = aggregate_slicewise.extract_metric(dummy_data_and_labels[0], labels=dummy_data_and_labels[1],
label_struc=dummy_data_and_labels[2], id_label=0,
indiv_labels_ids=[0, 1], perslice=False, method='max')
assert agg_metric[list(agg_metric)[0]]['MAX()'] == 41.0
def test_extract_metric(dummy_data_and_labels):
# TODO: test with combined labels
"""Test different estimation methods."""
# Weighted average
agg_metric = aggregate_slicewise.extract_metric(
dummy_data_and_labels[0],
labels=dummy_data_and_labels[1],
label_struc=dummy_data_and_labels[2],
id_label=0,
perslice=False,
method='wa')
assert agg_metric[list(agg_metric)[0]]['WA()'] == 38.0
# Binarized mask
agg_metric = aggregate_slicewise.extract_metric(
dummy_data_and_labels[0],
labels=dummy_data_and_labels[1],
label_struc=dummy_data_and_labels[2],
id_label=0,
perslice=False,
method='bin')
assert agg_metric[list(agg_metric)[0]]['BIN()'] == pytest.approx(36.66,
rel=0.01)
# Maximum Likelihood
agg_metric = aggregate_slicewise.extract_metric(
dummy_data_and_labels[0],
labels=dummy_data_and_labels[1],
label_struc=dummy_data_and_labels[2],
id_label=0,
indiv_labels_ids=[0, 1, 2],
perslice=False,
method='ml')
assert agg_metric[list(agg_metric)[0]]['ML()'] == pytest.approx(39.9,
rel=0.01)
# Maximum A Posteriori
agg_metric = aggregate_slicewise.extract_metric(
dummy_data_and_labels[0],
labels=dummy_data_and_labels[1],
label_struc=dummy_data_and_labels[2],
id_label=0,
indiv_labels_ids=[0, 1, 2],
perslice=False,
method='map')
assert agg_metric[list(agg_metric)[0]]['MAP()'] == pytest.approx(40.0,
rel=0.01)
# Maximum
agg_metric = aggregate_slicewise.extract_metric(
dummy_data_and_labels[0],
labels=dummy_data_and_labels[1],
label_struc=dummy_data_and_labels[2],
id_label=0,
indiv_labels_ids=[0, 1],
perslice=False,
method='max')
assert agg_metric[list(agg_metric)[0]]['MAX()'] == 41.0
def test_extract_metric_2d(dummy_data_and_labels_2d):
"""Test different estimation methods."""
agg_metric = aggregate_slicewise.extract_metric(
dummy_data_and_labels_2d[0],
labels=dummy_data_and_labels_2d[1],
label_struc=dummy_data_and_labels_2d[2],
id_label=0,
indiv_labels_ids=0,
perslice=False,
method='wa')
assert agg_metric[list(agg_metric)[0]]['WA()'] == 5.0
def test_save_as_csv_extract_metric(dummy_data_and_labels):
"""Test file output with extract_metric()"""
# With input label file
agg_metric = aggregate_slicewise.extract_metric(dummy_data_and_labels[0], labels=dummy_data_and_labels[1],
label_struc=dummy_data_and_labels[2], id_label=0,
indiv_labels_ids=[0, 1], perslice=False, method='wa')
aggregate_slicewise.save_as_csv(agg_metric, 'tmp_file_out.csv')
with open('tmp_file_out.csv', 'r') as csvfile:
spamreader = csv.reader(csvfile, delimiter=',')
next(spamreader) # skip header
assert next(spamreader)[1:-1] == [sct.__version__, '', '0:4', '', 'label_0', '2.5', '38.0']
def test_save_as_csv_extract_metric(dummy_data_and_labels):
"""Test file output with extract_metric()"""
# With input label file
agg_metric = aggregate_slicewise.extract_metric(dummy_data_and_labels[0], labels=dummy_data_and_labels[1],
label_struc=dummy_data_and_labels[2], id_label=0,
indiv_labels_ids=[0, 1], perslice=False, method='wa')
aggregate_slicewise.save_as_csv(agg_metric, 'tmp_file_out.csv')
with open('tmp_file_out.csv', 'r') as csvfile:
spamreader = csv.reader(csvfile, delimiter=',')
next(spamreader) # skip header
assert next(spamreader)[1:-1] == [__version__, '', '0:4', '', 'label_0', '2.5', '38.0']
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=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
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):
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:
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
display_open(fname_output)
def main(argv=None):
parser = get_parser()
arguments = parser.parse_args(argv)
verbose = arguments.v
set_loglevel(verbose=verbose)
param_default = Param()
overwrite = 0 # TODO: Not used. Why?
fname_data = get_absolute_path(arguments.i)
path_label = arguments.f
method = arguments.method
fname_output = arguments.o
append_csv = arguments.append
combine_labels = arguments.combine
labels_user = arguments.l
adv_param_user = arguments.param # TODO: Not used. Why?
slices = parse_num_list(arguments.z)
levels = parse_num_list(arguments.vert)
fname_vertebral_labeling = arguments.vertfile
perslice = arguments.perslice
perlevel = arguments.perlevel
fname_normalizing_label = arguments.norm_file # TODO: Not used. Why?
normalization_method = arguments.norm_method # TODO: Not used. Why?
label_to_fix = arguments.fix_label # TODO: Not used. Why?
fname_output_metric_map = arguments.output_map # TODO: Not used. Why?
fname_mask_weight = arguments.mask_weighted # TODO: Not used. Why?
discard_negative_values = int(arguments.discard_neg_val) # TODO: Not used. Why?
# 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=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
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 not levels:
fname_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):
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:
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=fname_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
display_open(fname_output)
def test_extract_metric_2d(dummy_data_and_labels_2d):
"""Test different estimation methods."""
agg_metric = aggregate_slicewise.extract_metric(dummy_data_and_labels_2d[0], labels=dummy_data_and_labels_2d[1],
label_struc=dummy_data_and_labels_2d[2], id_label=0,
indiv_labels_ids=0, perslice=False, method='wa')
assert agg_metric[list(agg_metric)[0]]['WA()'] == 5.0
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)
