BUAN Bundle Shape Similarity Score

This example explains how we can use BUAN [1] 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.data import two_cingulum_bundles
from dipy.segment.bundles import (
    bundle_shape_similarity,
    select_random_set_of_streamlines,
)
from dipy.viz import actor, window

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.default_rng()
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(1.0, 1, 1)
    for i, bundle in enumerate(bundles):
        color = colors[i]
        streamtube_actor = actor.streamtube(bundle, colors=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=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",
)

Two Cingulum Bundles.

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 = [0]

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=clust_thr, threshold=threshold
)
print("Shape similarity score = ", ba_score)

Shape similarity score =  0.6

Let’s change the value of threshold to 10.

threshold = 10

ba_score = bundle_shape_similarity(
    bundle1, bundle2, rng, clust_thr=clust_thr, threshold=threshold
)
print("Shape similarity score = ", ba_score)

Shape similarity score =  0.9416666666666667

Higher value of threshold gives us higher shape similarity score as it is more lenient.

References

[1]

Bramsh Qamar Chandio, Shannon Leigh Risacher, Franco Pestilli, Daniel Bullock, Fang-Cheng Yeh, Serge Koudoro, Ariel Rokem, Jaroslaw Harezlak, and Eleftherios Garyfallidis. Bundle analytics, a computational framework for investigating the shapes and profiles of brain pathways across populations. Scientific Reports, 10:17149, October 2020. URL: https://doi.org/10.1038/s41598-020-74054-4, doi:10.1038/s41598-020-74054-4.