Cleaning Parameters

This page documents the configuration options for controlling bundle cleaning in pyAFQ. Note that this goes inside of segmentation_params.

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(
        cleaning_params=dict(
            distance_threshold=5,
            length_threshold=5,  # More lenient cleaning
        ))
    )

Cleaning Parameter Reference

AFQ.recognition.cleaning.clean_bundle(tg, n_points=100, clean_rounds=5, distance_threshold=4, length_threshold=4, min_sl=20, stat=<function mean>, core_only=0.6, return_idx=False, remove_lengths='long')

Clean a segmented fiber group based on the Mahalnobis distance of each streamline

Parameters:

tgStatefulTractogram class instance or ArraySequence

A whole-brain tractogram to be segmented.

n_pointsint, optional

Number of points to resample streamlines to. Default: 100

clean_roundsint, optional.

Number of rounds of cleaning based on the Mahalanobis distance from the mean of extracted bundles. Default: 5

distance_thresholdfloat, optional.

Threshold of cleaning based on the Mahalanobis distance (the units are standard deviations). Default: 4.

length_threshold: float, optional

Threshold for cleaning based on length (in standard deviations). Length of any streamline should not be more than this number of stdevs from the mean length.

min_slint, optional.

Number of streamlines in a bundle under which we will not bother with cleaning outliers. Default: 20.

statcallable or str, optional.

The statistic of each node relative to which the Mahalanobis is calculated. Default: np.mean (but can also use median, etc.)

core_onlyfloat, optional

If non-zero, only the core of the bundle is used for cleaning. The core is defined as the middle 60% of each streamline, thus our default is 0.6. This means streamlines are allowed to deviate in the starting and ending 20% of the bundle. This is useful for allowing more diverse endpoints. Default: 0.6

return_idxbool

Whether to return indices in the original streamlines. Default: False.

remove_lengthsstr

Specifies which streamlines to remove based on their length. Options are “long” (remove long streamlines), “short” (remove short streamlines), or “both” (remove both long and short streamlines). Default: “long”

Returns:

A StatefulTractogram class instance containing only the streamlines

that have a Mahalanobis distance smaller than clean_threshold from

the mean of each one of the nodes.