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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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