Segmentation Parameters#

This page documents the configuration options for controlling tractography in pyAFQ. These parameters can be set in your configuration file or passed as arguments when using the API.

Example Usage#

from AFQ.api.group import GroupAFQ
import AFQ.data.fetch as afd
import os.path as op

afd.organize_stanford_data()

myafq = GroupAFQ(
    bids_path=op.join(afd.afq_home, 'stanford_hardi'),
    preproc_pipeline='vistasoft',
    segmentation_params=dict(
        dist_to_waypoint=10,
        dist_to_atlas=10)
    )

Segmentation Parameter Reference#

AFQ.recognition.recognize.recognize(tg, img, mapping, bundle_dict, reg_template, nb_points=False, nb_streamlines=False, clip_edges=False, parallel_segmentation={'engine': 'serial'}, rb_recognize_params={'model_clust_thr': 1.25, 'pruning_thr': 12, 'reduction_thr': 25}, refine_reco=False, prob_threshold=0, dist_to_waypoint=None, rng=None, return_idx=False, filter_by_endpoints=True, dist_to_atlas=4, save_intermediates=None, cleaning_params={})[source]

Segment streamlines into bundles.

Parameters

tgstr, StatefulTractogram: Tractogram to segment.
imgstr, nib.Nifti1Image: Image for reference.
mappingMappingDefinition: Mapping from subject to template.
bundle_dictdict or AFQ.api.BundleDict: Dictionary of bundles to segment.
reg_templatestr, nib.Nifti1Image: Template image for registration.
nb_pointsint, boolean: Resample streamlines to nb_points number of points. If False, no resampling is done. Default: False
nb_streamlinesint, boolean: Subsample streamlines to nb_streamlines. If False, no subsampling is don. Default: False
clip_edgesbool: Whether to clip the streamlines to be only in between the ROIs. Default: False
parallel_segmentationdict or AFQ.api.BundleDict: How to parallelize segmentation across processes when performing waypoint ROI segmentation. Set to {“engine”: “serial”} to not perform parallelization. Some engines may cause errors, depending on the system. See dipy.utils.parallel.paramap for details. Default: {“engine”: “serial”}
rb_recognize_paramsdict: RecoBundles parameters for the recognize function. Default: dict(model_clust_thr=1.25, reduction_thr=25, pruning_thr=12)
refine_recobool: Whether to refine the RecoBundles segmentation. Default: False
prob_thresholdfloat.: Using AFQ Algorithm. Initial cleaning of fiber groups is done using probability maps from [Hua2008]. Here, we choose an average probability that needs to be exceeded for an individual streamline to be retained. Default: 0.
dist_to_waypointfloat.: The distance that a streamline node has to be from the waypoint ROI in order to be included or excluded. If set to None (default), will be calculated as the center-to-corner distance of the voxel in the diffusion data. If a bundle has inc_addtol or exc_addtol in its bundle_dict, that tolerance will be added to this distance. For example, if you wanted to increase tolerance for the right arcuate waypoint ROIs by 3 each, you could make the following modification to your bundle_dict: bundle_dict[“Right Arcuate”][“inc_addtol”] = [3, 3] Additional tolerances can also be negative. Default: None.
rngRandomState or int: If None, creates RandomState. If int, creates RandomState with seed rng. Used in RecoBundles Algorithm. Default: None.
return_idxbool: Whether to return the indices in the original streamlines as part of the output of segmentation. Default: False.
filter_by_endpoints: bool: Whether to filter the bundles based on their endpoints. Default: True.
dist_to_atlasfloat: If filter_by_endpoints is True, this is the required distance from the endpoints to the atlas ROIs. Default: 4
save_intermediatesstr, optional: The full path to a folder into which intermediate products are saved. Default: None, means no saving of intermediates.
cleaning_paramsdict, optional: Cleaning params to pass to seg.clean_bundle. This will override the default parameters of that method. However, this can be overriden by setting the cleaning parameters in the bundle_dict. Default: {}.

References

Hua2008: Hua K, Zhang J, Wakana S, Jiang H, Li X, et al. (2008) Tract probability maps in stereotaxic spaces: analyses of white matter anatomy and tract-specific quantification. Neuroimage 39: 336-347
Yeatman2012: Yeatman, Jason D., Robert F. Dougherty, Nathaniel J. Myall, Brian A. Wandell, and Heidi M. Feldman. 2012. “Tract Profiles of White Matter Properties: Automating Fiber-Tract Quantification” PloS One 7 (11): e49790.
Garyfallidis2018: Garyfallidis et al. Recognition of white matter bundles using local and global streamline-based registration and clustering, Neuroimage, 2017.