Abstract #1030
How to avoid biased streamlines-based metrics for streamlines with variable step sizes
Jean-Christophe Houde 1 , Marc-Alexandre Ct-Harnois 1 , and Maxime Descoteaux 1
1
Computer Science department, Universit de
Sherbrooke, Sherbrooke, Quebec, Canada
We show that metrics computed over streamlines can
easily be biased or incorrect for streamlines with a
step size that is too large or variable. The basic
methods to compute those statistics, sometimes called
Tractometry methods, generally only use the points of
the streamlines to sample the corresponding image
volumes. However, for streamlines where the step size is
too large or variable, this sampling is skewed, and
derived metrics are biased. We present a simple updated
method that correctly handles those streamlines, and we
show that metrics computed using this method are robust
to the streamline sampling.
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