Event Abstract

Using Ontologies for the Visualization of Hierarchical Neuroanatomical Structures

  • 1 Zuse Institute Berlin, Germany
  • 2 Free University of Berlin, Germany

Introduction:
Anatomical atlases along with semantic information are used to store information of anatomy in biology and medicine. Existing work on atlases using semantics concentrates on retrieving information from knowledge bases. This often requires expert knowledge of for example relation types or specific query languages. Atlas visualization approaches, so far, either provide limited query possibilities or require a lot of user interaction.

We present a method for generating intuitive visualizations for high-level user queries to an hierarchical surface-based neuroantomical atlas. We compute focus and context in an ontology with a specific level-of-detail-strategy for hierarchical structures. We demonstrate our method on a 3D surface-based averaged atlas of the bee brain which contains structures ranging from neuropils down to presynaptic swellings of nerve cells. The visualizations can be used for education and presentation.

Procedure:
The core steps of our method are: (1) Development of an ontology with a specific structure which is linked to geometries. (2) Definition of high-level visualization queries specifying a set of relevant relations. (3) User selection of a focus object and a visualization query. (4) Generation of query-dependent importance values for each structure. (5) Mapping of importance values to visualization parameters (transparency). Figure 1 illustrates the procedure.

The classes of our ontology represent hierarchical organized parts of the bee brain. Our geometries are integrated as instances of the classes. The modeling is based on the Foundational Model of Anatomy (FMA) [2]. 1200 relations of 20 relation types and 100 classes coupled to 300 instances are contained in the current ontology version.

Together with neurobiologists we set scale values for classes, visibility values for instances, and we defined visualization queries: The scale value is set to ensure the correct ordering of the structures through the different levels of detail. It can for example distinguish between neuropils (coarse scale) and neurons (finer scale). The visibility value encodes if and how structures at a certain hierarchy level are visualized when their substructures are the focus of the current visualization. It is useful for the instantiation of individual data with incomplete substructures [1]. Further we developed scenarios where a meaningful visualization is desired. For each scenario, we defined relevant structures that should be depicted by the visualization. Formalized queries were derived and mapped to a set of important relations that are required to derive the desired visualization.

For the visualization of a structure the user selects a query and defines how much detail and context information shall be used. Based on these inputs our algorithm starts to spread decreasing importance values among structures that are connected via query relations to the focus. Then the resulting importancies are mapped to visualization parameters.

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References

1. Kuß, A., Prohaska, S., Meyer, B., Rybak, J., Hege, H.-C. 2008. Ontology-based visualization of hierarchical neuroanatomical structures. Proceedings of VCBM 2008, C. Botha, G. Kindlemann, W.J. Niessen, B. Preim, Eds., 177-184.

2. Rosse, C., Mejino, J. 2008. The foundational model of anatomy ontology. Anatomy Ontologies for Bioinformatics. Principle and Prectice, A. Burger, D. Davidson, R. Baldock, Ed. Springer.

Keywords: Hierarchical Neuroanatomical Structures

Conference: Neuroinformatics 2009, Pilsen, Czechia, 6 Sep - 8 Sep, 2009.

Presentation Type: Poster Presentation

Topic: Digital atlasing

Citation: Kuss A, Prohaska S and Rybak J (2009). Using Ontologies for the Visualization of Hierarchical Neuroanatomical Structures. Front. Neur. Conference Abstract: Neuroinformatics 2009. doi: 10.3389/conf.neuro.11.2009.08.017

Received: 21 May 2009; Published Online: 21 May 2009.

* Correspondence: Dr. Anja Kuss, Zuse Institute Berlin, Berlin, Germany, kuss@zib.de

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