Interactive Similarity Analysis for 3D+t Cell Trajectory Data

by Jens Fangerau

Institution: Universit├Ąt Heidelberg
Department: The Faculty of Mathematics and Computer Science
Degree: PhD
Year: 2015
Record ID: 1109283
Full text PDF: http://www.ub.uni-heidelberg.de/archiv/18095


Recent data acquisition techniques permit an improved analysis of living organisms. These techniques produce 3D+t information of cell developments in unprecedentedly high resolution. Biologists have a strong desire to analyze these cell evolutions in order to find similarities in their migration and division behaviors. The exploration of such patterns helps them in understanding how cells and hence organisms are able to ensure a regular shape development. However, the enormous size of the time-dependent data with several tens of thousands of cells and the need to analyze it in 3D hinder an interactive analysis. Visualizing the data to identify and extract relevant features provides a solution to this problem. For this, new visualization approaches are required that reduce the complexity of the data to detect important features in the visual analysis. In this thesis, novel visual similarity analysis methods are presented to interactively process very large 3D+t data of cell developments. Three main methods are developed that allow different visual analysis strategies. The usefulness of them is demonstrated by applications to cells from zebrafish embryos and Arabidopsis thaliana plants. Both data sets feature a high regularity in the shape formation of the organs and domain experts seek to research similar cell behaviors that are responsible for this development. For example, the identification of 3D division behaviors in plants is still an unresolved issue. The first method is a novel visualization approach that can automatically classify cell division types in plant data sets with high memory and time efficiency. The visualization is based on the generation of newly introduced cell isosurfaces that allow a quantitative and spatial comparison of cell division behaviors among individual plants. The method is applied to cells of the lateral root of Arabidopsis plants and reveals similar division schemes with respect to their temporal order. The second method enables a new visual similarity analysis for arbitrary 3D trajectory data in order to extract similar movement behaviors. The algorithm performs a grouping of thousands of trajectories with an optional level of detail modification. The clustering is based on a newly weighted combination of geometry and migratory features for which the weights are used to emphasize feature combinations. As a result, similar collective cell movements in zebrafish as well as a hitherto unknown correlation between division types and subsequent nuclei migrations in the Arabidopsis plants are detected. The third method is a novel visualization technique called the structure map. It permits a compact and interactive similarity analysis of thousands of binary tree structures. Unique trees are pre-ordered in the map based on spectral similarities and substructures are highlighted according to user-selected tree descriptors. Applied to cell developments from zebrafish depicted as trees, the map achieves compression rates up to 95% according to spectral analysis and facilitates an immediate…