Abstract #3071

Assessment of Rotationally-Invariant Clustering Using Streamlet Tractography

Matthew George Liptrot^1,2 and Francois Lauze¹

¹Department of Computer Science, University of Copenhagen, Copenhagen, Denmark, ²DTU Compute, Technical University of Denmark, Lyngby, Denmark

We present a novel visualisation-based strategy for the assessment of a recently proposed clustering technique for raw DWI volumes which derives rotationally-invariant metrics to classify voxels. The validity of the division of all brain tissue voxels into such classes was assessed using the recently developed streamlets visualisation technique, which aims to represent brain fibres by collections of many short streamlines. Under the assumption that streamlines seeded in a cluster should stay within it, we were able to assess how well perceptual tracing could occur across the boundaries of the clusters.

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