Visualizing the Structure of Large Trees
Burcu Aydin, Gabor Pataki, Haonan Wang, Alim Ladha, Elizabeth Bullitt,, J.S. Marron

TL;DR
This paper presents a novel 2D visualization method for large tree structures, aiding in detecting discrepancies and improving data quality in complex datasets like brain artery systems.
Contribution
A new graphical representation of trees with covariate coding that helps identify data inconsistencies in large, complex tree-structured datasets.
Findings
Enabled quick detection of discrepancies in brain artery data
Led to data modifications improving analysis significance
Demonstrated effectiveness in medical imaging data cleaning
Abstract
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
