Using RecoBundles for tract delineation
To delineate the major white matter pathways, pyAFQ defaults to use the waypoint ROI approach described in Yeatman, 2012. However, as an alternative approach, pyAFQ also supports using the RecoBundles algorithm Garyfallidis, 2018,which uses an atlas of bundles in streamlines.
import os.path as op
from paths import afq_homefrom AFQ.api.group import GroupAFQ
import AFQ.api.bundle_dict as abd
import plotly1Defining 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
use abd.reco_bd(16), which tells pyAFQ to use the RecoBundles
algorithm for bundle recognition.
myafq = GroupAFQ(
output_dir=op.join(afq_home, 'stanford_hardi', 'derivatives',
'recobundles'),
bids_path=op.join(afq_home, 'stanford_hardi'),
# Set the algorithm to use RecoBundles for bundle recognition:
bundle_info=abd.reco_bd(16),
preproc_pipeline='vistasoft')
myafq.export("profiles")INFO:AFQ:No seed mask given, using FA (or first scalar if none are FA)thresholded to 0.2
INFO:AFQ:No stop mask given, using FA (or first scalar if none are FA)thresholded to 0.2
{'01': '/home/jovyan/data/tractometry/tractometry/stanford_hardi/derivatives/recobundles/sub-01/ses-01/dwi/sub-01_ses-01_desc-profiles_tractography.csv'}plotly.io.show(myafq.export("all_bundles_figure")["01"][0])Loading...
- Yeatman, J. D., Dougherty, R. F., Myall, N. J., Wandell, B. A., & Feldman, H. M. (2012). Tract profiles of white matter properties: automating fiber-tract quantification. PLoS One, 7(11), e49790. 10.1371/journal.pone.0049790
- Garyfallidis, E., Côté, M.-A., Rheault, F., Sidhu, J., Hau, J., Petit, L., Fortin, D., Cunanne, S., & Descoteaux, M. (2018). Recognition of white matter bundles using local and global streamline-based registration and clustering. NeuroImage, 170, 283–295. 10.1016/j.neuroimage.2017.07.015