  This study introduces a new method of visualizing complex tree structured
objects. The usefulness of this method is illustrated in the context of
detecting unexpected features in a data set of very large trees. The major
contribution is a novel two-dimensional graphical representation of each tree,
with a covariate coded by color. The motivating data set contains three
dimensional representations of brain artery systems of 105 subjects. Due to
inaccuracies inherent in the medical imaging techniques, issues with the
reconstruction algo- rithms and inconsistencies introduced by manual
adjustment, various discrepancies are present in the data. The proposed
representation enables quick visual detection of the most common discrepancies.
For our driving example, this tool led to the modification of 10% of the artery
trees and deletion of 6.7%. The benefits of our cleaning method are
demonstrated through a statistical hypothesis test on the effects of aging on
vessel structure. The data cleaning resulted in improved significance levels.
