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.
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
To show the concept we will use two pre-saved cingulum bundle. Let’s start by fetching the data.
cb_subj1, _ = two_cingulum_bundles()
Let’s create two streamline sets (bundles) from same bundle cb_subj1 by randomly selecting 60 streamlines two times.
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)
Now, let’s visualize two bundles.
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) elif fname is not None: window.record(scene, out_path=fname, size=(900, 900)) show_both_bundles([bundle1, bundle2], colors=[(1, 0, 0), (0, 1, 0)], show=False, fname="two_bundles.png")
Calculate shape similarity score between two bundles.
0 cluster_thr because we want to use all streamlines and not the centroids of clusters.
clust_thr = 
Threshold indicates how strictly we want two bundles to be similar in shape.
threshold = 5 ba_score = bundle_shape_similarity(bundle1, bundle2, rng, clust_thr, threshold) print("Shape similarity score = ", ba_score)
Let’s change the value of threshold to 10.
threshold = 10 ba_score = bundle_shape_similarity(bundle1, bundle2, rng, clust_thr, threshold) print("Shape similarity score = ", ba_score)
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
the full source code of this example. This same script is also included in the dipy source distribution under the