This example explains how we can use BUAN [Chandio2020] to calculate shape
similarity between two given bundles. Where, shape similarity score of 1 means
two bundles are extremely close in shape and 0 implies no shape similarity
whatsoever. Shape similarity score can be used to compare populations or individuals.
It can also serve as a quality assurance metric, to validate streamline
registration quality, bundle extraction quality by calculating output with a
reference bundle or other issues with pre-processing by calculating shape
dissimilarity with a reference bundle. First import the necessary modules. To show the concept we will use two pre-saved cingulum bundle.
Let’s start by fetching the data. Let’s create two streamline sets (bundles) from same bundle cb_subj1 by
randomly selecting 60 streamlines two times. Now, let’s visualize two bundles. Calculate shape similarity score between two bundles. 0 cluster_thr because we want to use all streamlines and not the centroids of
clusters. Threshold indicates how strictly we want two bundles to be similar in shape. Let’s change the value of threshold to 10. Higher value of threshold gives us higher shape similarity score as it is
more lenient. Chandio, B.Q., Risacher, S.L., Pestilli, F.,
Bullock, D., Yeh, FC., Koudoro, S., Rokem, A., Harezlak, J., and
Garyfallidis, E. Bundle analytics, a computational framework for
investigating the shapes and profiles of brain pathways across
populations. Sci Rep 10, 17149 (2020) Example source code You can download BUAN Bundle Shape Similarity Score
import numpy as np
from dipy.viz import window, actor
from dipy.segment.bundles import bundle_shape_similarity
from dipy.segment.bundles import select_random_set_of_streamlines
from dipy.data import two_cingulum_bundles
cb_subj1, _ = two_cingulum_bundles()
rng = np.random.RandomState()
bundle1 = select_random_set_of_streamlines(cb_subj1, 60, rng=None)
bundle2 = select_random_set_of_streamlines(cb_subj1, 60, rng=None)
def show_both_bundles(bundles, colors=None, show=True, fname=None):
scene = window.Scene()
scene.SetBackground(1., 1, 1)
for (i, bundle) in enumerate(bundles):
color = colors[i]
streamtube_actor = actor.streamtube(bundle, color, linewidth=0.3)
streamtube_actor.RotateX(-90)
streamtube_actor.RotateZ(90)
scene.add(streamtube_actor)
if show:
window.show(scene)
if fname is not None:
window.record(scene, n_frames=1, out_path=fname, size=(900, 900))
show_both_bundles([bundle1, bundle2], colors=[(1, 0, 0), (0, 1, 0)],
show=False, fname="two_bundles.png")
clust_thr = [0]
threshold = 5
ba_score = bundle_shape_similarity(bundle1, bundle2, rng, clust_thr, threshold)
print("Shape similarity score = ", ba_score)
threshold = 10
ba_score = bundle_shape_similarity(bundle1, bundle2, rng, clust_thr, threshold)
print("Shape similarity score = ", ba_score)
References
the full source code of this example
. This same script is also included in the dipy source distribution under the doc/examples/
directory.