Tractography Clustering with QuickBundles

This example explains how we can use QuickBundles [Garyfallidis12] to simplify/cluster streamlines.

First import the necessary modules.

import numpy as np
from dipy.segment.clustering import QuickBundles
from dipy.io.pickles import save_pickle
from dipy.data import get_fnames
from dipy.viz import window, actor, colormap


For educational purposes we will try to cluster a small streamline bundle known from neuroanatomy as the fornix.

fname = get_fnames('fornix')


fornix = load_tractogram(fname, 'same', bbox_valid_check=False)
streamlines = fornix.streamlines


Perform QuickBundles clustering using the MDF metric and a 10mm distance threshold. Keep in mind that since the MDF metric requires streamlines to have the same number of points, the clustering algorithm will internally use a representation of streamlines that have been automatically downsampled/upsampled so they have only 12 points (To set manually the number of points, see Resample Feature).

qb = QuickBundles(threshold=10.)
clusters = qb.cluster(streamlines)


clusters is a ClusterMap object which contains attributes that provide information about the clustering result.

print("Nb. clusters:", len(clusters))
print("Cluster sizes:", map(len, clusters))
print("Small clusters:", clusters < 10)
print("Streamlines indices of the first cluster:\n", clusters[0].indices)
print("Centroid of the last cluster:\n", clusters[-1].centroid)

Nb. clusters: 4
Cluster sizes: <map object at 0x2bdd6dc70>
Small clusters: [False False False  True]
Streamlines indices of the first cluster:
[0, 7, 8, 10, 11, 12, 13, 14, 15, 18, 26, 30, 33, 35, 41, 65, 66, 85, 100, 101, 105, 115, 116, 119, 122, 123, 124, 125, 126, 128, 129, 135, 139, 142, 143, 144, 148, 151, 159, 167, 175, 180, 181, 185, 200, 208, 210, 224, 237, 246, 249, 251, 256, 267, 270, 280, 284, 293, 296, 297, 299]
Centroid of the last cluster:
[[ 84.83774  117.9259    77.322784]
[ 86.108505 115.84363   81.91885 ]
[ 86.40357  112.25677   85.7293  ]
[ 86.48337  107.60328   88.137825]
[ 86.238976 102.51007   89.29447 ]
[ 85.04564   97.460205  88.542404]
[ 82.6024    93.14851   86.84209 ]
[ 78.98937   89.57682   85.63652 ]
[ 74.72344   86.60828   84.939186]
[ 70.40846   85.158745  82.4484  ]
[ 66.745346  86.002625  78.82582 ]
[ 64.02451   88.43942   75.06974 ]]

Nb. clusters: 4

Cluster sizes: [64, 191, 47, 1]

Small clusters: array([False, False, False, True], dtype=bool)

Streamlines indices of the first cluster:
[0, 7, 8, 10, 11, 12, 13, 14, 15, 18, 26, 30, 33, 35, 41, 65, 66, 85, 100,
101, 105, 115, 116, 119, 122, 123, 124, 125, 126, 128, 129, 135, 139, 142,
143, 144, 148, 151, 159, 167, 175, 180, 181, 185, 200, 208, 210, 224, 237,
246, 249, 251, 256, 267, 270, 280, 284, 293, 296, 297, 299]

Centroid of the last cluster:
array([[  84.83773804,  117.92590332,   77.32278442],
[  86.10850525,  115.84362793,   81.91885376],
[  86.40357208,  112.25676727,   85.72930145],
[  86.48336792,  107.60327911,   88.13782501],
[  86.23897552,  102.5100708 ,   89.29447174],
[  85.04563904,   97.46020508,   88.54240417],
[  82.60240173,   93.14851379,   86.84208679],
[  78.98937225,   89.57682037,   85.63652039],
[  74.72344208,   86.60827637,   84.9391861 ],
[  70.40846252,   85.15874481,   82.4484024 ],
[  66.74534607,   86.00262451,   78.82582092],
[  64.02451324,   88.43942261,   75.0697403 ]], dtype=float32)


clusters also has attributes such as centroids (cluster representatives), and methods like add, remove, and clear to modify the clustering result.

Let’s first show the initial dataset.

# Enables/disables interactive visualization
interactive = False

scene = window.Scene()
scene.SetBackground(1, 1, 1)
window.record(scene, out_path='fornix_initial.png', size=(600, 600))
if interactive:
window.show(scene)


Show the centroids of the fornix after clustering (with random colors):

colormap = colormap.create_colormap(np.arange(len(clusters)))

scene.clear()
scene.SetBackground(1, 1, 1)
window.record(scene, out_path='fornix_centroids.png', size=(600, 600))
if interactive:
window.show(scene)


Show the labeled fornix (colors from centroids).

colormap_full = np.ones((len(streamlines), 3))
for cluster, color in zip(clusters, colormap):
colormap_full[cluster.indices] = color

scene.clear()
scene.SetBackground(1, 1, 1)
window.record(scene, out_path='fornix_clusters.png', size=(600, 600))
if interactive:
window.show(scene)


It is also possible to save the complete ClusterMap object with pickling.

save_pickle('QB.pkl', clusters)


Finally, here is a video of QuickBundles applied on a larger dataset.

References

Garyfallidis E. et al., QuickBundles a method for tractography simplification, Frontiers in Neuroscience, vol 6, no 175, 2012.

Total running time of the script: ( 0 minutes 0.293 seconds)

Gallery generated by Sphinx-Gallery