Cleaning Parameters

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