def test_streamline_mapping():
streamlines = [np.array([[0, 0, 0], [0, 0, 0], [0, 2, 2]], 'float'),
np.array([[0, 0, 0], [0, 1, 1], [0, 2, 2]], 'float'),
np.array([[0, 2, 2], [0, 1, 1], [0, 0, 0]], 'float')]
mapping = streamline_mapping(streamlines, (1, 1, 1))
expected = {(0, 0, 0): [0, 1, 2], (0, 2, 2): [0, 1, 2],
(0, 1, 1): [1, 2]}
assert_equal(mapping, expected)
mapping = streamline_mapping(streamlines, (1, 1, 1),
mapping_as_streamlines=True)
expected = dict((k, [streamlines[i] for i in indices])
for k, indices in expected.items())
assert_equal(mapping, expected)
# Test passing affine
affine = np.eye(4)
affine[:3, 3] = .5
mapping = streamline_mapping(streamlines, affine=affine,
mapping_as_streamlines=True)
assert_equal(mapping, expected)
# Make the voxel size smaller
affine = np.diag([.5, .5, .5, 1.])
affine[:3, 3] = .25
expected = dict((tuple(i*2 for i in key), value)
for key, value in expected.items())
mapping = streamline_mapping(streamlines, affine=affine,
mapping_as_streamlines=True)
assert_equal(mapping, expected)
def test_streamline_mapping():
streamlines = [
np.array([[0, 0, 0], [0, 0, 0], [0, 2, 2]], 'float'),
np.array([[0, 0, 0], [0, 1, 1], [0, 2, 2]], 'float'),
np.array([[0, 2, 2], [0, 1, 1], [0, 0, 0]], 'float')
]
mapping = streamline_mapping(streamlines, affine=np.eye(4))
expected = {(0, 0, 0): [0, 1, 2], (0, 2, 2): [0, 1, 2], (0, 1, 1): [1, 2]}
npt.assert_equal(mapping, expected)
mapping = streamline_mapping(streamlines,
affine=np.eye(4),
mapping_as_streamlines=True)
expected = dict((k, [streamlines[i] for i in indices])
for k, indices in expected.items())
npt.assert_equal(mapping, expected)
# Test passing affine
affine = np.eye(4)
affine[:3, 3] = .5
mapping = streamline_mapping(streamlines,
affine=affine,
mapping_as_streamlines=True)
npt.assert_equal(mapping, expected)
# Make the voxel size smaller
affine = np.diag([.5, .5, .5, 1.])
affine[:3, 3] = .25
expected = dict(
(tuple(i * 2 for i in key), value) for key, value in expected.items())
mapping = streamline_mapping(streamlines,
affine=affine,
mapping_as_streamlines=True)
npt.assert_equal(mapping, expected)
def compute_dsc(estimated_tract, true_tract):
aff=np.array([[-1.25, 0, 0, 90],[0, 1.25, 0, -126],[0, 0, 1.25, -72],[0, 0, 0, 1]])
voxel_list_estimated_tract = streamline_mapping(estimated_tract, affine=aff).keys()
voxel_list_true_tract = streamline_mapping(true_tract, affine=aff).keys()
TP = len(set(voxel_list_estimated_tract).intersection(set(voxel_list_true_tract)))
vol_A = len(set(voxel_list_estimated_tract))
vol_B = len(set(voxel_list_true_tract))
DSC = 2.0 * float(TP) / float(vol_A + vol_B)
return DSC
def voxelCount():
tractography, header = trackvis.read(T_A_filename)
tractography = [streamline[0] for streamline in tractography]
affine = utils.affine_for_trackvis(voxel_size=np.array([2, 2, 2]))
print("---Number of voxel---")
print(len(streamline_mapping(T_A, affine=affine).keys()))
tkinter.messagebox.showinfo(
"Voxel", len(streamline_mapping(T_A, affine=affine).keys()))
def dsc(estimated_tract, true_tract):
"""Compute the overlap between the segmented tract and ground truth tract
"""
aff = np.array([[-1.25, 0, 0, 90], [0, 1.25, 0, -126], [0, 0, 1.25, -72],
[0, 0, 0, 1]])
voxel_list_estimated_tract = streamline_mapping(estimated_tract,
affine=aff).keys()
voxel_list_true_tract = streamline_mapping(true_tract, affine=aff).keys()
TP = len(
set(voxel_list_estimated_tract).intersection(
set(voxel_list_true_tract)))
vol_A = len(set(voxel_list_estimated_tract))
vol_B = len(set(voxel_list_true_tract))
DSC = 2.0 * float(TP) / float(vol_A + vol_B)
return DSC
def compute_voxel_measures(estimated_tract, true_tract, aff):
#aff=np.array([[-1.25, 0, 0, 90],[0, 1.25, 0, -126],[0, 0, 1.25, -72],[0, 0, 0, 1]])
voxel_list_estimated_tract = streamline_mapping(estimated_tract,
affine=aff).keys()
voxel_list_true_tract = streamline_mapping(true_tract, affine=aff).keys()
n_ET = len(estimated_tract)
n_TT = len(true_tract)
dictionary_ET = streamline_mapping(estimated_tract, affine=aff)
dictionary_TT = streamline_mapping(true_tract, affine=aff)
voxel_list_intersection = set(voxel_list_estimated_tract).intersection(
set(voxel_list_true_tract))
if n_ET == 0:
return 0, 0, 0, 0, 0, len(set(voxel_list_true_tract))
sum_int_ET = 0
sum_int_TT = 0
for k in voxel_list_intersection:
sum_int_ET = sum_int_ET + len(dictionary_ET[k])
sum_int_TT = sum_int_TT + len(dictionary_TT[k])
sum_int_ET = sum_int_ET / n_ET
sum_int_TT = sum_int_TT / n_TT
sum_ET = 0
for k in voxel_list_estimated_tract:
sum_ET = sum_ET + len(dictionary_ET[k])
sum_ET = sum_ET / n_ET
sum_TT = 0
for k in voxel_list_true_tract:
sum_TT = sum_TT + len(dictionary_TT[k])
sum_TT = sum_TT / n_TT
TP = len(
set(voxel_list_estimated_tract).intersection(
set(voxel_list_true_tract)))
vol_A = len(set(voxel_list_estimated_tract))
vol_B = len(set(voxel_list_true_tract))
FP = vol_B - TP
FN = vol_A - TP
sensitivity = float(TP) / float(TP + FN)
DSC = 2.0 * float(TP) / float(vol_A + vol_B)
wDSC = float(sum_int_ET + sum_int_TT) / float(sum_ET + sum_TT)
J = float(TP) / float(TP + FN + FP)
return DSC, wDSC, J, sensitivity, TP, FP, FN
def compute_volume(bundle):
"""Compute voxel volume of a bundle.
"""
aff = np.array([[-1.25, 0, 0, 90], [0, 1.25, 0, -126], [0, 0, 1.25, -72],
[0, 0, 0, 1]])
voxel_list = streamline_mapping(bundle, affine=aff).keys()
vol_bundle = len(set(voxel_list))
return vol_bundle
def compute_X_fa(superset, exID, subjID, nb_points_fa=100):
"""Compute a matrix with dimension (len(superset), nb_points_fa)
that contains the FA profile of the oriented superset.
"""
superset = set_number_of_points(superset, nb_points_fa)
oriented_superset = orient_tract_kmeans(superset)
X_fa = np.zeros((len(superset), nb_points_fa))
fa_nii = nib.load('%s/aligned_FA/FA_m%s_s%s.nii.gz' %
(subjID, exID, subjID))
aff = fa_nii.affine
fa = fa_nii.get_data()
for s in range(len(oriented_superset) - 1):
voxel_list = streamline_mapping(oriented_superset[s:s + 1],
affine=aff).keys()
for v, (i, j, k) in enumerate(voxel_list):
X_fa[s, v] = fa[i, j, k]
return X_fa
T_A_filename = 'F:\Thesis\Resources\\alldata\\201111_uf.right.trk'
threshold_short_streamlines = 0.0
color=colors.red
T_A, hdr = loadtrkfile(T_A_filename, threshold_short_streamlines=threshold_short_streamlines)
countstreamlines()
showhistogram()
tractography,header=trackvis.read(T_A_filename)
tractography = [streamline[0] for streamline in tractography]
affine=utils.affine_for_trackvis(voxel_size=np.array([2,2,2]))
print ("---Number of voxel---")
print (len(streamline_mapping(T_A,affine=affine).keys()))
show_tract(T_A, color)
