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
# subjects_dir = "/home/brain/workingdir/data/dwi/hcp/preprocessed/response_dhollander/101107"
# hemi = 'lh'
# surf = 'white'
# alpha = 1
# brain = Brain(subjects_dir=subjects_dir, subject_id=subject_id, hemi=hemi, surf=surf, alpha=alpha)
# brain.add_overlay(l_label_value, min=l_label_value[l_label_value > 0].min(),
# max=l_label_value.max(), sign='pos', hemi='lh', name='lh')
#
# mlab.show()
tck_path = '/home/brain/workingdir/data/dwi/hcp/preprocessed/' \
'response_dhollander/101107/Diffusion/1M_20_01_20dynamic250_SD_Stream_single.tck'
fasciculus = Fasciculus(tck_path)
streamlines = fasciculus.get_data()
print len(streamlines)
streamlines = fasciculus.sort_streamlines()
s0 = [s[0] for s in streamlines]
stream_terminus_lh = np.array(s0)
dist_lh = cdist(coords[l_label_value > 0], stream_terminus_lh)
lh_stream_index = np.array(len(streamlines) * [False])
for i in range(len(dist_lh[:])):
temp_index = np.array(dist_lh[i] <= 3)
lh_stream_index += temp_index
lh_rois_streamlines = streamlines[lh_stream_index]
fasciculus.set_data(lh_rois_streamlines)
out_path = '/home/brain/workingdir/data/dwi/hcp/preprocessed/' \
'response_dhollander/101107/Diffusion/1M_20_01_20dynamic250_SD_Stream_lh_occipital3.tck'
fasciculus.save2tck(out_path)
