def _process_streamlines(streamlines):
# Compute the bounding boxes and indices for all streamlines.
mins = []
maxs = []
offset_streamlines = []
# Offset the streamlines to compute the indices only in the bounding box.
# Reduces memory use later on.
for idx, s in enumerate(streamlines):
mins.append(np.min(s.astype(int), 0))
maxs.append(np.max(s.astype(int), 0) + 1)
offset_streamlines.append((s - mins[-1]).astype(np.float32))
offset_streamlines = ArraySequence(offset_streamlines)
indices = uncompress(offset_streamlines)
return mins, maxs, indices
def compute_connectivity_matrix(streamlines, atlas_file, allow_self_connection,
minimize_self_connection, lower_triangular,
normalize):
atlas_img = nib.load(atlas_file)
atlas_data = atlas_img.get_data().astype(np.int32)
real_labels = np.unique(atlas_data)
nb_real_labels = real_labels.shape[0]
label_mapping = {real_labels[i]: i for i in range(nb_real_labels)}
con_mat = np.zeros((nb_real_labels, nb_real_labels), dtype=np.uint32)
indices = uncompress(streamlines, return_mapping=False)
for strl_indices in indices:
logging.debug("\nStarting streamline")
logging.debug(strl_indices)
in_label, out_label = intersects_two_rois_atlas(
atlas_data, strl_indices, allow_self_connection,
minimize_self_connection)
if in_label is not None and out_label is not None:
in_index = label_mapping[in_label]
out_index = label_mapping[out_label]
con_mat[in_index, out_index] += 1
if in_label != out_label:
con_mat[out_index, in_index] += 1
con_mat = con_mat[1:, 1:]
if lower_triangular:
con_mat = np.tril(con_mat)
if normalize:
con_mat = con_mat / np.sum(con_mat)
str_labels = ["{}".format(k) for k in sorted(label_mapping.keys())]
final_mat = pd.DataFrame(data=con_mat,
columns=str_labels[1:],
index=str_labels[1:])
return final_mat
def cut_between_masks_streamlines(sft, binary_mask, min_len=0):
""" Cut streamlines so their segment are within the bounding box
or going from binary mask #1 to binary mask #2.
This function erases the data_per_point and data_per_streamline.
Parameters
----------
sft: StatefulTractogram
The sft to cut streamlines (using a single mask with 2 entities) from.
binary_mask: np.ndarray
Boolean array representing the region (must contain 2 entities)
min_len: float
Minimum length from the resulting streamlines.
Returns
-------
new_sft : StatefulTractogram
New object with the streamlines trimmed within the masks.
"""
sft.to_vox()
sft.to_corner()
streamlines = sft.streamlines
density = get_endpoints_density_map(streamlines, binary_mask.shape)
density[density > 0] = 1
density[binary_mask == 0] = 0
roi_data_1, roi_data_2 = split_heads_tails_kmeans(binary_mask)
new_streamlines = []
(indices, points_to_idx) = uncompress(streamlines, return_mapping=True)
for strl_idx, strl in enumerate(streamlines):
strl_indices = indices[strl_idx]
in_strl_idx, out_strl_idx = intersects_two_rois(
roi_data_1, roi_data_2, strl_indices)
if in_strl_idx is not None and out_strl_idx is not None:
points_to_indices = points_to_idx[strl_idx]
tmp = compute_streamline_segment(strl, strl_indices, in_strl_idx,
out_strl_idx, points_to_indices)
new_streamlines.append(tmp)
new_sft = StatefulTractogram.from_sft(new_streamlines, sft)
return filter_streamlines_by_length(new_sft, min_length=min_len)
def cut_streamlines(streamlines, roi_anat_1, roi_anat_2):
roi_img_1 = nb.load(roi_anat_1)
affine1 = roi_img_1.get_affine()
roi_data_1 = roi_img_1.get_data()
non_zero_1 = np.transpose(np.nonzero(roi_data_1))
non_zero_1_set = set(map(tuple, non_zero_1))
roi_img_2 = nb.load(roi_anat_2)
affine2 = roi_img_2.get_affine()
if not np.allclose(affine1, affine2):
raise ValueError("The affines of both ROIs do not match.")
roi_data_2 = roi_img_2.get_data()
non_zero_2 = np.transpose(np.nonzero(roi_data_2))
non_zero_2_set = set(map(tuple, non_zero_2))
overlap = non_zero_1_set & non_zero_2_set
if len(overlap) > 0:
logging.warning('Parts of the ROIs may overlap.\n' +
'Behavior might be unexpected.')
final_streamlines = []
(indices, points_to_idx) = uncompress(streamlines, return_mapping=True)
for strl_idx, strl in enumerate(streamlines):
logging.debug("Starting streamline")
strl_indices = indices[strl_idx]
logging.debug(strl_indices)
in_strl_idx, out_strl_idx = intersects_two_rois(
roi_data_1, roi_data_2, strl_indices)
if in_strl_idx is not None and out_strl_idx is not None:
points_to_indices = points_to_idx[strl_idx]
logging.debug(points_to_indices)
final_streamlines.append(
compute_streamline_segment(strl, strl_indices, in_strl_idx,
out_strl_idx, points_to_indices))
return final_streamlines
def main():
parser = _build_arg_parser()
args = parser.parse_args()
assert_inputs_exist(parser, [args.in_tractogram, args.in_labels],
args.reference)
assert_outputs_exist(parser, args, args.out_hdf5)
# HDF5 will not overwrite the file
if os.path.isfile(args.out_hdf5):
os.remove(args.out_hdf5)
if (args.save_raw_connections or args.save_intermediate
or args.save_discarded) and not args.out_dir:
parser.error('To save outputs in the streamlines form, provide the '
'output directory using --out_dir.')
if args.out_dir:
if os.path.abspath(args.out_dir) == os.getcwd():
parser.error('Do not use the current path as output directory.')
assert_output_dirs_exist_and_empty(parser,
args,
args.out_dir,
create_dir=True)
log_level = logging.WARNING
if args.verbose:
log_level = logging.INFO
logging.basicConfig(level=log_level)
coloredlogs.install(level=log_level)
set_sft_logger_level('WARNING')
img_labels = nib.load(args.in_labels)
data_labels = get_data_as_label(img_labels)
real_labels = np.unique(data_labels)[1:]
if args.out_labels_list:
np.savetxt(args.out_labels_list, real_labels, fmt='%i')
# Voxel size must be isotropic, for speed/performance considerations
vox_sizes = img_labels.header.get_zooms()
if not np.allclose(np.mean(vox_sizes), vox_sizes, atol=1e-03):
parser.error('Labels must be isotropic')
logging.info('*** Loading streamlines ***')
time1 = time.time()
sft = load_tractogram_with_reference(parser,
args,
args.in_tractogram,
bbox_check=False)
sft.remove_invalid_streamlines()
time2 = time.time()
logging.info(' Loading {} streamlines took {} sec.'.format(
len(sft), round(time2 - time1, 2)))
if not is_header_compatible(sft, img_labels):
raise IOError('{} and {}do not have a compatible header'.format(
args.in_tractogram, args.in_labels))
sft.to_vox()
sft.to_corner()
# Get all streamlines intersection indices
logging.info('*** Computing streamlines intersection ***')
time1 = time.time()
indices, points_to_idx = uncompress(sft.streamlines, return_mapping=True)
time2 = time.time()
logging.info(' Streamlines intersection took {} sec.'.format(
round(time2 - time1, 2)))
# Compute the connectivity mapping
logging.info('*** Computing connectivity information ***')
time1 = time.time()
con_info = compute_connectivity(indices, data_labels, real_labels,
extract_longest_segments_from_profile)
time2 = time.time()
logging.info(' Connectivity computation took {} sec.'.format(
round(time2 - time1, 2)))
# Prepare directories and information needed to save.
_create_required_output_dirs(args)
logging.info('*** Starting connection post-processing and saving. ***')
logging.info(' This can be long, be patient.')
time1 = time.time()
# Saving will be done from streamlines already in the right space
comb_list = list(itertools.combinations(real_labels, r=2))
comb_list.extend(zip(real_labels, real_labels))
iteration_counter = 0
with h5py.File(args.out_hdf5, 'w') as hdf5_file:
affine, dimensions, voxel_sizes, voxel_order = get_reference_info(sft)
hdf5_file.attrs['affine'] = affine
hdf5_file.attrs['dimensions'] = dimensions
hdf5_file.attrs['voxel_sizes'] = voxel_sizes
hdf5_file.attrs['voxel_order'] = voxel_order
# Each connections is processed independently. Multiprocessing would be
# a burden on the I/O of most SSD/HD
for in_label, out_label in comb_list:
if iteration_counter > 0 and iteration_counter % 100 == 0:
logging.info('Split {} nodes out of {}'.format(
iteration_counter, len(comb_list)))
iteration_counter += 1
pair_info = []
if in_label not in con_info:
continue
elif out_label in con_info[in_label]:
pair_info.extend(con_info[in_label][out_label])
if out_label not in con_info:
continue
elif in_label in con_info[out_label]:
pair_info.extend(con_info[out_label][in_label])
if not len(pair_info):
continue
connecting_streamlines = []
connecting_ids = []
for connection in pair_info:
strl_idx = connection['strl_idx']
curr_streamlines = compute_streamline_segment(
sft.streamlines[strl_idx], indices[strl_idx],
connection['in_idx'], connection['out_idx'],
points_to_idx[strl_idx])
connecting_streamlines.append(curr_streamlines)
connecting_ids.append(strl_idx)
# Each step is processed from the previous 'success'
# 1. raw -> length pass/fail
# 2. length pass -> loops pass/fail
# 3. loops pass -> outlier detection pass/fail
# 4. outlier detection pass -> qb curvature pass/fail
# 5. qb curvature pass == final connections
connecting_streamlines = ArraySequence(connecting_streamlines)
raw_dps = sft.data_per_streamline[connecting_ids]
raw_sft = StatefulTractogram.from_sft(connecting_streamlines,
sft,
data_per_streamline=raw_dps,
data_per_point={})
_save_if_needed(raw_sft, hdf5_file, args, 'raw', 'raw', in_label,
out_label)
# Doing all post-processing
if not args.no_pruning:
valid_length_ids, invalid_length_ids = _prune_segments(
raw_sft.streamlines, args.min_length, args.max_length,
vox_sizes[0])
invalid_length_sft = raw_sft[invalid_length_ids]
valid_length = connecting_streamlines[valid_length_ids]
_save_if_needed(invalid_length_sft, hdf5_file, args,
'discarded', 'invalid_length', in_label,
out_label)
else:
valid_length = connecting_streamlines
valid_length_ids = range(len(connecting_streamlines))
if not len(valid_length):
continue
valid_length_sft = raw_sft[valid_length_ids]
_save_if_needed(valid_length_sft, hdf5_file, args, 'intermediate',
'valid_length', in_label, out_label)
if not args.no_remove_loops:
no_loop_ids = remove_loops_and_sharp_turns(
valid_length, args.loop_max_angle)
loop_ids = np.setdiff1d(np.arange(len(valid_length)),
no_loop_ids)
loops_sft = valid_length_sft[loop_ids]
no_loops = valid_length[no_loop_ids]
_save_if_needed(loops_sft, hdf5_file, args, 'discarded',
'loops', in_label, out_label)
else:
no_loops = valid_length
no_loop_ids = range(len(valid_length))
if not len(no_loops):
continue
no_loops_sft = valid_length_sft[no_loop_ids]
_save_if_needed(no_loops_sft, hdf5_file, args, 'intermediate',
'no_loops', in_label, out_label)
if not args.no_remove_outliers:
outliers_ids, inliers_ids = remove_outliers(
no_loops,
args.outlier_threshold,
nb_samplings=10,
fast_approx=True)
outliers_sft = no_loops_sft[outliers_ids]
inliers = no_loops[inliers_ids]
_save_if_needed(outliers_sft, hdf5_file, args, 'discarded',
'outliers', in_label, out_label)
else:
inliers = no_loops
inliers_ids = range(len(no_loops))
if not len(inliers):
continue
inliers_sft = no_loops_sft[inliers_ids]
_save_if_needed(inliers_sft, hdf5_file, args, 'intermediate',
'inliers', in_label, out_label)
if not args.no_remove_curv_dev:
no_qb_curv_ids = remove_loops_and_sharp_turns(
inliers,
args.loop_max_angle,
use_qb=True,
qb_threshold=args.curv_qb_distance)
qb_curv_ids = np.setdiff1d(np.arange(len(inliers)),
no_qb_curv_ids)
qb_curv_sft = inliers_sft[qb_curv_ids]
_save_if_needed(qb_curv_sft, hdf5_file, args, 'discarded',
'qb_curv', in_label, out_label)
else:
no_qb_curv_ids = range(len(inliers))
no_qb_curv_sft = inliers_sft[no_qb_curv_ids]
_save_if_needed(no_qb_curv_sft, hdf5_file, args, 'final', 'final',
in_label, out_label)
time2 = time.time()
logging.info(
' Connections post-processing and saving took {} sec.'.format(
round(time2 - time1, 2)))
def main():
parser = build_args_parser()
args = parser.parse_args()
assert_inputs_exist(parser, [args.tracks, args.labels])
if os.path.abspath(args.output) == os.getcwd():
parser.error('Do not use the current path as output directory.')
assert_output_dirs_exist_and_empty(parser, args, args.output)
log_level = logging.WARNING
if args.verbose:
log_level = logging.INFO
logging.basicConfig(level=log_level)
img_labels = nb.load(args.labels)
if not np.issubdtype(img_labels.get_data_dtype().type, np.integer):
parser.error("Label image should contain integers for labels.")
# Ensure that voxel size is isotropic. Currently, for speed considerations,
# we take the length in voxel space and multiply by the voxel size. For
# this to work correctly, voxel size must be isotropic.
vox_sizes = img_labels.header.get_zooms()
if not np.mean(vox_sizes) == vox_sizes[0]:
parser.error('Labels must be isotropic')
if np.min(img_labels.get_data()) < 0 or \
np.max(img_labels.get_data()) > args.max_labels:
parser.error('Invalid labels in labels image')
logging.info('*** Loading streamlines ***')
time1 = time.time()
sft = load_tractogram_with_reference(parser, args, args.in_tractogram)
sft.to_vox()
sft.to_corner()
time2 = time.time()
logging.info(' Number of streamlines to process: {}'.format(
len(sft.streamlines)))
logging.info(' Loading streamlines took %0.3f ms',
(time2 - time1) * 1000.0)
# Get all streamlines intersection indices
logging.info('*** Computing streamlines intersection ***')
time1 = time.time()
indices, points_to_idx = uncompress(sft.streamlines, return_mapping=True)
time2 = time.time()
logging.info(' Streamlines intersection took %0.3f ms',
(time2 - time1) * 1000.0)
# Compute the connectivity mapping
logging.info('*** Computing connectivity information ***')
time1 = time.time()
con_info = compute_connectivity(indices, img_labels.get_data(),
extract_longest_segments_from_profile)
time2 = time.time()
logging.info(' Connectivity computation took %0.3f ms',
(time2 - time1) * 1000.0)
# Symmetrize matrix
final_con_info = _symmetrize_con_info(con_info)
# Prepare directories and information needed to save.
saving_opts = _get_saving_options(args)
out_paths = _get_output_paths(args.output)
_create_required_output_dirs(out_paths, args)
# Here, we use nb_labels + 1 since we want the direct mapping from image
# label to matrix element. We will remove the first row and column before
# saving.
# TODO for other metrics
# dtype should be adjusted depending on the type of elements
# stored in the con_mat
nb_labels = args.max_labels
con_mat = np.zeros((nb_labels + 1, nb_labels + 1), dtype=np.uint32)
logging.info('*** Starting connection post-processing and saving. ***')
logging.info(' This can be long, be patient.')
time1 = time.time()
for in_label in list(final_con_info.keys()):
for out_label in list(final_con_info[in_label].keys()):
pair_info = final_con_info[in_label][out_label]
if not len(pair_info):
continue
final_strl = []
for connection in pair_info:
strl_idx = connection['strl_idx']
final_strl.append(
compute_streamline_segment(sft.streamlines[strl_idx],
indices[strl_idx],
connection['in_idx'],
connection['out_idx'],
points_to_idx[strl_idx]))
_save_if_needed(final_strl, args, saving_opts, out_paths, 'raw',
'raw', in_label, out_label)
# Doing all post-processing
if not args.no_pruning:
pruned_strl, invalid_strl = _prune_segments(
final_strl, args.min_length, args.max_length, vox_sizes[0])
_save_if_needed(invalid_strl, args, saving_opts, out_paths,
'discarded', 'removed_length', in_label,
out_label)
else:
pruned_strl = final_strl
if not len(pruned_strl):
continue
_save_if_needed(pruned_strl, args, saving_opts, out_paths,
'intermediate', 'pruned', in_label, out_label)
if not args.no_remove_loops:
no_loops, loops = remove_loops_and_sharp_turns(
pruned_strl, args.loop_max_angle)
_save_if_needed(loops, args, saving_opts, out_paths,
'discarded', 'loops', in_label, out_label)
else:
no_loops = pruned_strl
if not len(no_loops):
continue
_save_if_needed(no_loops, args, saving_opts, out_paths,
'intermediate', 'no_loops', in_label, out_label)
if not args.no_remove_outliers:
no_outliers, outliers = remove_outliers(
no_loops, args.outlier_threshold)
_save_if_needed(outliers, args, saving_opts, out_paths,
'discarded', 'outliers', in_label, out_label)
else:
no_outliers = no_loops
if not len(no_outliers):
continue
_save_if_needed(no_outliers, args, saving_opts, out_paths,
'intermediate', 'no_outliers', in_label, out_label)
if not args.no_remove_loops_again:
no_qb_loops_strl, loops2 = remove_loops_and_sharp_turns(
no_outliers, args.loop_max_angle, True,
args.loop_qb_distance)
_save_if_needed(loops2, args, saving_opts, out_paths,
'discarded', 'qb_loops', in_label, out_label)
else:
no_qb_loops_strl = no_outliers
_save_if_needed(no_qb_loops_strl, args, saving_opts, out_paths,
'final', 'final', in_label, out_label)
# TODO for other metrics
# This would be where this is modified and the value
# is computed (eg: mean FA in the connection.
con_mat[in_label, out_label] += len(no_qb_loops_strl)
time2 = time.time()
logging.info(' Connection post-processing and saving took %0.3f ms',
(time2 - time1) * 1000.0)
# Remove first line and column, since they are index 0 and
# would represent a connection to non-label voxels. Only used when
# post-processing to avoid unnecessary -1 on labels for each access.
con_mat = con_mat[1:, 1:]
np.save(os.path.join(args.output, 'final_matrix.npy'), con_mat)
def run_test(self):
tracto = Tractogram(streamlines=[self.strl_data, self.strl_data])
indices_new = uncompress(tracto.streamlines)
self.assertTrue(np.allclose(indices_new[0], self.gt_indices))
def main():
parser = build_arg_parser()
args = parser.parse_args()
assert_inputs_exist(parser, [args.in_tractogram, args.labels])
if os.path.abspath(args.output_dir) == os.getcwd():
parser.error('Do not use the current path as output directory.')
assert_output_dirs_exist_and_empty(parser, args, args.output_dir,
create_dir=True)
log_level = logging.WARNING
if args.verbose:
log_level = logging.INFO
logging.basicConfig(level=log_level)
coloredlogs.install(level=log_level)
set_sft_logger_level('WARNING')
img_labels = nib.load(args.labels)
data_labels = img_labels.get_fdata().astype(np.int16)
real_labels = np.unique(data_labels)[1:]
if args.out_labels_list:
np.savetxt(args.out_labels_list, real_labels, fmt='%i')
if not np.issubdtype(img_labels.get_data_dtype().type, np.integer):
parser.error("Label image should contain integers for labels.")
# Voxel size must be isotropic, for speed/performance considerations
vox_sizes = img_labels.header.get_zooms()
if not np.mean(vox_sizes) == vox_sizes[0]:
parser.error('Labels must be isotropic')
logging.info('*** Loading streamlines ***')
time1 = time.time()
sft = load_tractogram_with_reference(parser, args, args.in_tractogram)
time2 = time.time()
logging.info(' Loading {} streamlines took {} sec.'.format(
len(sft), round(time2 - time1, 2)))
logging.info('*** Filtering streamlines ***')
data_mask = np.zeros(data_labels.shape)
data_mask[data_labels > 0] = 1
original_len = len(sft)
time1 = time.time()
sft.to_vox()
sft.to_corner()
sft.remove_invalid_streamlines()
time2 = time.time()
logging.info(
' Discarded {} streamlines from filtering in {} sec.'.format(
original_len - len(sft), round(time2 - time1, 2)))
logging.info(' Number of streamlines to process: {}'.format(len(sft)))
# Get all streamlines intersection indices
logging.info('*** Computing streamlines intersection ***')
time1 = time.time()
indices, points_to_idx = uncompress(sft.streamlines, return_mapping=True)
time2 = time.time()
logging.info(' Streamlines intersection took {} sec.'.format(
round(time2 - time1, 2)))
# Compute the connectivity mapping
logging.info('*** Computing connectivity information ***')
time1 = time.time()
con_info = compute_connectivity(indices,
data_labels, real_labels,
extract_longest_segments_from_profile)
time2 = time.time()
logging.info(' Connectivity computation took {} sec.'.format(
round(time2 - time1, 2)))
# Prepare directories and information needed to save.
_create_required_output_dirs(args)
logging.info('*** Starting connection post-processing and saving. ***')
logging.info(' This can be long, be patient.')
time1 = time.time()
# Saving will be done from streamlines already in the right space
comb_list = list(itertools.combinations(real_labels, r=2))
comb_list.extend(zip(real_labels, real_labels))
iteration_counter = 0
for in_label, out_label in comb_list:
if iteration_counter > 0 and iteration_counter % 100 == 0:
logging.info('Split {} nodes out of {}'.format(iteration_counter,
len(comb_list)))
iteration_counter += 1
pair_info = []
if in_label not in con_info:
continue
elif out_label in con_info[in_label]:
pair_info.extend(con_info[in_label][out_label])
if out_label not in con_info:
continue
elif in_label in con_info[out_label]:
pair_info.extend(con_info[out_label][in_label])
if not len(pair_info):
continue
connecting_streamlines = []
for connection in pair_info:
strl_idx = connection['strl_idx']
curr_streamlines = compute_streamline_segment(
sft.streamlines[strl_idx],
indices[strl_idx],
connection['in_idx'],
connection['out_idx'],
points_to_idx[strl_idx])
connecting_streamlines.append(curr_streamlines)
_save_if_needed(connecting_streamlines, args, 'raw',
'raw', in_label, out_label)
# Doing all post-processing
if not args.no_pruning:
valid_length, invalid_length = _prune_segments(
connecting_streamlines,
args.min_length,
args.max_length,
vox_sizes[0])
_save_if_needed(invalid_length, args,
'discarded', 'invalid_length',
in_label, out_label)
else:
valid_length = connecting_streamlines
if not len(valid_length):
continue
_save_if_needed(valid_length, args,
'intermediate', 'valid_length', in_label, out_label)
if not args.no_remove_loops:
no_loop_ids = remove_loops_and_sharp_turns(valid_length,
args.loop_max_angle)
no_loops = [valid_length[i] for i in no_loop_ids]
loop_ids = np.setdiff1d(np.arange(len(valid_length)), no_loop_ids)
loops = [valid_length[i] for i in loop_ids]
_save_if_needed(loops, args,
'discarded', 'loops', in_label, out_label)
else:
no_loops = valid_length
if not len(no_loops):
continue
_save_if_needed(no_loops, args,
'intermediate', 'no_loops', in_label, out_label)
if not args.no_remove_outliers:
inliers, outliers = remove_outliers(no_loops,
args.outlier_threshold)
_save_if_needed(outliers, args,
'discarded', 'outliers', in_label, out_label)
else:
inliers = no_loops
if not len(inliers):
continue
_save_if_needed(inliers, args,
'intermediate', 'inliers', in_label, out_label)
if not args.no_remove_curv_dev:
no_qb_curv_ids = remove_loops_and_sharp_turns(
inliers,
args.loop_max_angle,
use_qb=True,
qb_threshold=args.curv_qb_distance)
no_qb_curv = [inliers[i] for i in no_qb_curv_ids]
qb_curv_ids = np.setdiff1d(
np.arange(len(inliers)), no_qb_curv_ids)
qb_curv = [inliers[i] for i in qb_curv_ids]
_save_if_needed(qb_curv, args,
'discarded', 'qb_curv', in_label, out_label)
else:
no_qb_curv = inliers
_save_if_needed(no_qb_curv, args,
'final', 'final', in_label, out_label)
time2 = time.time()
logging.info(
' Connections post-processing and saving took {} sec.'.format(
round(time2 - time1, 2)))