A visualization pipeline for large-scale tractography data

We present a novel methodology for clustering and visualizing large-scale tractography data sets. Tractography data sets contain hundreds of millions of line segments, making visualizing and understanding this data very difficult. Our method reduces and simplifies this data to create coherent groupings and visualizations. Our input is a collection of tracts, from which we derive metrics and perform clustering. Using the clustered data, we create a three-dimensional histogram that contains the counts of the number of tracts that intersect each bin. With these new data sets, we can perform standard visualization techniques. Our contribution is the visualization pipeline itself, as well as a study and evaluation schema. Our study utilizes our evaluation schema to identify the best and most influential clustering metrics, and an optimal number of clusters under varying user requirements.