Interactive Similarity Analysis and Error Detection in Large Tree Collections

Conference paper
Part of the Mathematics and Visualization book series (MATHVISUAL)


Automatic feature tracking is widely used for the analysis of time-dependent data. If the features exhibit splitting behavior, it is best characterized by tree-like tracks. For a large number of time steps, each with numerous features, these data become increasingly difficult to analyze. In this paper, we focus on the problem of comparing and contrasting hundreds to thousands of trees to support developmental biologists in their study of cell division patterns in embryos. To this end, we propose a new visual analytics method called structure map. This two-dimensional, color-coded map arranges trees into tiles along a Hilbert curve, preserving a tree similarity measure, which we define via graph Laplacians. The structure map supports both global and local analysis based on user-selected tree descriptors. It helps analysts identify similar trees, observe clustering and sizes of clusters within the forest, and detect outliers in a compact and uniform representation. We apply the structure map for analyzing 3D cell tracking from two periods of zebrafish embryogenesis: blastulation to early epiboly and tailbud extension. In both cases, we show how the structure map supported biologists to find systematic differences in the data set as well as detect erroneous cell behaviors.


Asymmetric Cell Division Tree Descriptor Hilbert Curve Stem Cell Division Tree Edit Distance 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Computer Graphics and VisualizationHeidelberg UniversityHeidelbergGermany
  2. 2.Centre for Organismal StudiesHeidelberg UniversityHeidelbergGermany
  3. 3.Scientific Visualization Group, ETH ZürichZürichSwitzerland

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