def muti_bundle_registration(paths_file, pts=12):
"""
muti-bundle registration and consolidation
Parameters
----------
paths_file: list; muti_bundle file path
pts: each streamline is divided into sections
Return
------
new header: include id of each streamline that comes from different subjects
registration and consolidation bundle
"""
fas = Fasciculus(paths_file[0])
# print fas.get_header()
bundle_header = {'fasciculus_id': None}
sub1 = fas.get_data()
bundle_header['fasciculus_id'] = len(sub1) * [
int(paths_file[0].split('/')[9])
]
sub2 = Fasciculus(paths_file[1]).get_data()
subj2_aligned = bundle_registration(sub1, sub2, pts=pts)
bundle = fas.fib_merge(sub1, subj2_aligned)
bundle_header['fasciculus_id'] += (
len(bundle) - len(sub1)) * [int(paths_file[1].split('/')[9])]
# print bundle_header
# print len(bundle)
for index in range(len(paths_file))[2:]:
# print paths_file[index]
sub = Fasciculus(paths_file[index]).get_data()
sub_aligned = bundle_registration(sub1, sub, pts=pts)
lenth = len(bundle)
bundle = fas.fib_merge(bundle, sub_aligned)
bundle_header['fasciculus_id'] += (
len(bundle) - lenth) * [int(paths_file[index].split('/')[9])]
fas.update_header(bundle_header)
fas.set_data(nibas.ArraySequence(bundle))
return fas
def cc_seg_pipeline(tck_path, mask, geo_path, thr=5., roi_id=None, step=0):
"""
Extract occipital streamlines and separate hemisphere streamlines
Parameters
----------
tck_path: list; streamline file path (.tck)
mask: surface mask (xh.aparc.annot)
geo_path: geometry data path (subject_id.L.white.native.surf.gii)
thr: distance of streamline terminus to surface
Return
------
occipital streamlines
"""
# load data
fasciculus = Fasciculus(tck_path)
# extract streamlines from left to right hemisphere
fibselection = FibSelection(fasciculus)
L_temp_need0 = fibselection.endpoint_dissimilarity()
fasciculus.set_data(L_temp_need0)
L_temp_need1 = fibselection.single_point_mid_sag()
fasciculus.set_data(L_temp_need1)
del L_temp_need0, L_temp_need1
gc.collect()
# separation of lr_seed fib
fib = fasciculus.hemi_fib_separation()
# choose the mask: Occipital
mask_fib = []
for i in range(len(fib)):
vertices, colortable, label = nib.freesurfer.read_annot(mask[i])
gii_data = nib.load(geo_path[i]).darrays
coords, faces = gii_data[0].data, gii_data[1].data
label_value = np.array(len(coords) * [0])
if not roi_id:
label_value[vertices == 11] = 11 # lateraloccipital
label_value[vertices == 13] = 13 # lingual
label_value[vertices == 5] = 5 # cuneus
label_value[vertices == 21] = 21 # pericalcarine
else:
for value in roi_id:
label_value[vertices == value] = value
# extract fib using mask
streamlines = fib[i]
streamlines = fasciculus.sort_streamlines(streamlines)
if i == 0:
s0 = [s[0] for s in streamlines]
else:
s0 = [s[-1] for s in streamlines]
stream_terminus = np.array(s0)
dist = cdist(coords[label_value > 0], stream_terminus)
stream_index = np.array(len(streamlines) * [False])
for j in range(len(dist[:])):
temp_index = np.array(dist[j] <= thr)
stream_index += temp_index
rois_streamlines = streamlines[stream_index]
mask_fib.append(rois_streamlines)
# lr fib merge
fib_merge = fasciculus.fib_merge(mask_fib[0], mask_fib[1])
del mask_fib
gc.collect()
fib_hemi = fasciculus.separation_fib_to_hemi(fib_merge)
del fib_merge
gc.collect()
return fib_hemi
