dipy_recobundles [-h] [–greater_than int] [–less_than int] [–no_slr]

[–clust_thr float] [–reduction_thr float] [–reduction_distance str] [–model_clust_thr float] [–pruning_thr float] [–pruning_distance str] [–slr_metric str] [–slr_transform str] [–slr_matrix str] [–refine] [–r_reduction_thr float] [–r_pruning_thr float] [–no_r_slr] [–out_dir str] [–out_recognized_transf str] [–out_recognized_labels str] streamline_files model_bundle_files

Recognize bundles

Positional Arguments

streamline_files The path of streamline files where you want to recognize bundles. model_bundle_files The path of model bundle files.

Optional Arguments

-h, --help

show this help message and exit

--greater_than int

Keep streamlines that have length greater than this value in mm.

--less_than int

Keep streamlines have length less than this value in mm.


Don’t enable local Streamline-based Linear Registration.

--clust_thr float

MDF distance threshold for all streamlines.

--reduction_thr float

Reduce search space by (mm).

--reduction_distance str

Reduction distance type can be mdf or mam.

--model_clust_thr float

MDF distance threshold for the model bundles.

--pruning_thr float

Pruning after matching.

--pruning_distance str

Pruning distance type can be mdf or mam.

--slr_metric str

Options are None, symmetric, asymmetric or diagonal.

--slr_transform str

Transformation allowed. translation, rigid, similarity or scaling.

--slr_matrix str

Options are ‘nano’, ‘tiny’, ‘small’, ‘medium’, ‘large’, ‘huge’.


Enable refine recognized bundle.

--r_reduction_thr float

Refine reduce search space by (mm).

--r_pruning_thr float

Refine pruning after matching.


Don’t enable Refine local Streamline-based Linear Registration.

Output Arguments(Optional)

--out_dir str

Output directory. (default current directory)

--out_recognized_transf str

Recognized bundle in the space of the model bundle.

--out_recognized_labels str

Indices of recognized bundle in the original tractogram.



Garyfallidis et al. Recognition of white matter bundles using local and global streamline-based registration and clustering, Neuroimage, 2017.


Chandio, B.Q., Risacher, S.L., Pestilli, F.,Bullock, D., Yeh, FC., Koudoro, S., Rokem, A., Harezlak, J., andGaryfallidis, E. Bundle analytics, a computational framework forinvestigating the shapes and profiles of brain pathways acrosspopulations. Sci Rep 10, 17149 (2020)

Garyfallidis, E., M. Brett, B. Amirbekian, A. Rokem, S. Van Der Walt, M. Descoteaux, and I. Nimmo-Smith. Dipy, a library for the analysis of diffusion MRI data. Frontiers in Neuroinformatics, 1-18, 2014.