Note
Go to the end to download the full example code
Using RecoBundles for bundle recognition#
For bundle recognition, pyAFQ defaults to use the waypoint ROI approach described in [Yeatman2012]. However, as an alternative approach, pyAFQ also supports using the RecoBundles algorithm [Garyfallidis2017], which uses an atlas of bundles in streamlines. This example shows how to use RecoBundles for bundle recognition.
The code closely resembles the code used in sphx_glr_tutorial_examples_plot_001-plot_afq_api.py.
import os.path as op
import AFQ.data.fetch as afd
from AFQ.api.group import GroupAFQ
afd.organize_stanford_data(clear_previous_afq="track")
tracking_params = dict(n_seeds=25000,
random_seeds=True,
rng_seed=42)
Defining the segmentation params#
We also refer to bundle recognition as the “segmentation” of the tractogram. Parameters of this process are set through a dictionary input to the segmentation_params argument of the GroupAFQ object. In this case, we set the seg_algo to “reco”, which tells pyAFQ to use the RecoBundles algorithm for bundle recognition.
myafq = GroupAFQ(
output_dir=op.join(afd.afq_home, 'stanford_hardi', 'derivatives',
'recobundles'),
bids_path=op.join(afd.afq_home, 'stanford_hardi'),
# Set the algorithm to use RecoBundles for bundle recognition:
segmentation_params={'seg_algo': 'reco'},
preproc_pipeline='vistasoft',
tracking_params=tracking_params,
viz_backend_spec='plotly_no_gif')
fig_files = myafq.export_all()
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Optimizing level 2 [max iter: 10000]
Optimizing level 1 [max iter: 1000]
Optimizing level 0 [max iter: 100]
Optimizing level 2 [max iter: 10000]
Optimizing level 1 [max iter: 1000]
Optimizing level 0 [max iter: 100]
Optimizing level 2 [max iter: 10000]
Optimizing level 1 [max iter: 1000]
Optimizing level 0 [max iter: 100]
0it [00:00, ?it/s]
20it [00:00, 198.03it/s]
44it [00:00, 220.38it/s]
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References:#
- Garyfallidis2017
Garyfallidis, Eleftherios, Marc-Alexandre Côté, Francois Rheault, Jasmeen Sidhu, Janice Hau, Laurent Petit, David Fortin, Stephen Cunanne, and Maxime Descoteaux. 2017.“Recognition of White Matter Bundles Using Local and Global Streamline-Based Registration and Clustering.”NeuroImage 170: 283-295.
Total running time of the script: (15 minutes 29.493 seconds)
Estimated memory usage: 2192 MB