def single_lap(moving_tractogram, static_tractogram, kdt, prototypes, example, k=500, distance_func = bundles_distances_mam):
"""Code for LAP from a single example.
"""
print("Retrieving affine from dictionary.")
table_filename = 'affine_dictionary.pickle'
table = pickle.load(open(table_filename))
moving_tractogram_basename = ntpath.basename(moving_tractogram)
static_tractogram_basename = ntpath.basename(static_tractogram)
affine = table[moving_tractogram_basename, static_tractogram_basename].items()[0][1]
print("Applying the affine to the example bundle.")
example_bundle = nib.streamlines.load(example)
example_bundle = example_bundle.streamlines
example_bundle_aligned = np.array([apply_affine(affine, s) for s in example_bundle])
print("Compute the dissimilarity of the aligned example bundle with the prototypes of target tractogram.")
example_bundle_aligned = np.array(example_bundle_aligned, dtype=np.object)
dm_example_bundle_aligned = distance_func(example_bundle_aligned, prototypes)
print("Segmentation as Rectangular linear Assignment Problem (RLAP).")
static_tractogram = nib.streamlines.load(static_tractogram)
static_tractogram = static_tractogram.streamlines
estimated_bundle_idx, min_cost_values = RLAP(kdt, k, dm_example_bundle_aligned, example_bundle_aligned, static_tractogram, distance_func)
estimated_bundle = static_tractogram[estimated_bundle_idx]
return estimated_bundle_idx, min_cost_values, len(example_bundle)
def nn_single_example(moving_tractogram, static_tractogram, example):
"""Code for NN from a single example.
"""
np.random.seed(0)
with open('config.json') as f:
data = json.load(f)
step_size = data["step_size"]
distance_func = bundles_distances_mam
subjID = ntpath.basename(static_tractogram)[0:6]
exID = ntpath.basename(example)[0:6]
example_bundle = nib.streamlines.load(example)
example_bundle = example_bundle.streamlines
example_bundle_res = resample_tractogram(example_bundle, step_size)
print(
"Retrieving the affine slr transformation for example %s and target %s."
% (exID, subjID))
affine = np.load('affine_m%s_s%s.npy' % (exID, subjID))
print("Applying the affine to the example bundle.")
example_bundle_aligned = np.array(
[apply_affine(affine, s) for s in example_bundle_res])
print(
"Compute the dissimilarity representation of the target tractogram and build the kd-tree."
)
static_tractogram = nib.streamlines.load(static_tractogram)
static_tractogram = static_tractogram.streamlines
static_tractogram_res = resample_tractogram(static_tractogram, step_size)
static_tractogram = static_tractogram_res
if isfile('prototypes.npy') & isfile('kdt'):
print("Retrieving past results for kdt and prototypes.")
kdt_filename = 'kdt'
kdt = pickle.load(open(kdt_filename))
prototypes = np.load('prototypes.npy')
else:
kdt, prototypes = compute_kdtree_and_dr_tractogram(static_tractogram)
#Saving files
kdt_filename = 'kdt'
pickle.dump(kdt,
open(kdt_filename, 'w'),
protocol=pickle.HIGHEST_PROTOCOL)
np.save('prototypes', prototypes)
print(
"Compute the dissimilarity of the aligned example bundle with the prototypes of target tractogram."
)
example_bundle_aligned = np.array(example_bundle_aligned, dtype=np.object)
dm_example_bundle_aligned = distance_func(example_bundle_aligned,
prototypes)
print("Segmentation as Nearest Neighbour (NN).")
estimated_bundle_idx, min_cost_values = NN(kdt, 1,
dm_example_bundle_aligned)
estimated_bundle = static_tractogram[estimated_bundle_idx]
return estimated_bundle_idx, min_cost_values, len(example_bundle)
def lap_single_example(moving_tractogram, static_tractogram, example):
"""Code for LAP from a single example.
"""
k = 500
distance_func = bundles_distances_mam
print("Computing the affine slr transformation.")
affine = tractograms_slr(moving_tractogram, static_tractogram)
print("Applying the affine to the example bundle.")
example_bundle = nib.streamlines.load(example)
example_bundle = example_bundle.streamlines
example_bundle_aligned = np.array(
[apply_affine(affine, s) for s in example_bundle])
print(
"Compute the dissimilarity representation of the target tractogram and build the kd-tree."
)
static_tractogram = nib.streamlines.load(static_tractogram)
static_tractogram = static_tractogram.streamlines
kdt, prototypes = compute_kdtree_and_dr_tractogram(static_tractogram)
print(
"Compute the dissimilarity of the aligned example bundle with the prototypes of target tractogram."
)
example_bundle_aligned = np.array(example_bundle_aligned, dtype=np.object)
dm_example_bundle_aligned = distance_func(example_bundle_aligned,
prototypes)
print("Segmentation as Rectangular linear Assignment Problem (RLAP).")
estimated_bundle_idx, min_cost_values = RLAP(kdt, k,
dm_example_bundle_aligned,
example_bundle_aligned,
static_tractogram,
distance_func)
estimated_bundle = static_tractogram[estimated_bundle_idx]
return estimated_bundle_idx, min_cost_values, len(example_bundle)
def lap_single_example(moving_tractogram, static_tractogram, example, lD, lE,
lR):
"""Code for LAP from a single example.
"""
np.random.seed(0)
with open('config.json') as f:
data = json.load(f)
k = data["k"]
step_size = data["step_size"]
tag = data["_inputs"][2]["datatype_tags"][0].encode("utf-8")
distance_func = bundles_distances_mam
subjID = ntpath.basename(static_tractogram)[0:6]
tract_name = ntpath.basename(example)[7:-10]
exID = ntpath.basename(example)[0:6]
example_bundle = nib.streamlines.load(example)
example_bundle = example_bundle.streamlines
example_bundle_res = resample_tractogram(example_bundle, step_size)
print(
"Retrieving the affine slr transformation for example %s and target %s."
% (exID, subjID))
affine = np.load('affine_m%s_s%s.npy' % (exID, subjID))
print("Applying the affine to the example bundle.")
example_bundle_aligned = np.array(
[apply_affine(affine, s) for s in example_bundle_res])
print(
"Compute the dissimilarity representation of the target tractogram and build the kd-tree."
)
static_tractogram = nib.streamlines.load(static_tractogram)
static_tractogram = static_tractogram.streamlines
static_tractogram_res = resample_tractogram(static_tractogram, step_size)
static_tractogram = static_tractogram_res
if isfile('prototypes.npy') & isfile('kdt'):
print("Retrieving past results for kdt and prototypes.")
kdt_filename = 'kdt'
kdt = pickle.load(open(kdt_filename))
prototypes = np.load('prototypes.npy')
else:
kdt, prototypes = compute_kdtree_and_dr_tractogram(static_tractogram)
#Saving files
kdt_filename = 'kdt'
pickle.dump(kdt,
open(kdt_filename, 'w'),
protocol=pickle.HIGHEST_PROTOCOL)
np.save('prototypes', prototypes)
print("Computing superset with k = %s" % k)
superset_idx = compute_superset(example_bundle_aligned,
kdt,
prototypes,
k=k)
print("Loading the two-waypoint ROIs of the target...")
table_filename = 'ROIs_labels_dictionary.pickle'
table = pickle.load(open(table_filename))
roi1_lab = table[tract_name].items()[0][1]
roi2_lab = table[tract_name].items()[1][1]
if tag == 'afq':
roi1_filename = 'aligned_ROIs/sub-%s_var-AFQ_lab-%s_roi.nii.gz' % (
subjID, roi1_lab)
roi2_filename = 'aligned_ROIs/sub-%s_var-AFQ_lab-%s_roi.nii.gz' % (
subjID, roi2_lab)
elif tag == 'wmaSeg':
roi1_filename = 'aligned_ROIs/%s.nii.gz' % roi1_lab
roi2_filename = 'aligned_ROIs/%s.nii.gz' % roi2_lab
roi1 = nib.load(roi1_filename)
roi2 = nib.load(roi2_filename)
print("Computing matrices for LAP...")
distance_matrix, endpoint_matrix, roi_matrix = compute_lap_matrices(
superset_idx, example_bundle_aligned, static_tractogram, roi1, roi2,
subjID, exID)
print("Using lambdaD = %s, lambdaE = %s and lambdaR = %s" % (lD, lE, lR))
estimated_bundle_idx, min_cost_values = RLAP_modified(
distance_matrix, endpoint_matrix, roi_matrix, superset_idx, lD, lE, lR)
return estimated_bundle_idx, min_cost_values, len(example_bundle)