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3D object retrieval in an atlas of neuronal structures

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Abstract

Circuit neuroscience tries to solve one of the most challenging questions in biology: How does the brain work? An important step toward an answer to this question is to gather detailed knowledge about the neuronal circuits of the model organism Drosophila melanogaster. Geometric representations of neuronal objects of the Drosophila are acquired using molecular genetic methods, confocal microscopy, nonrigid registration and segmentation. These objects are integrated into a constantly growing common atlas. The comparison of new segmented neuronal objects to already known neuronal structures is a frequent task, which evolves with a growing amount of data into a bottleneck of the knowledge discovery process. Thus, the exploration of the atlas by means of domain specific similarity measures becomes a pressing need. To enable similarity based retrieval of neuronal objects, we defined together with domain experts tailored dissimilarity measures for each of the three typical neuronal structures cell body, projection, and arborization. Moreover, we defined the neuron enhanced similarity for projections and arborizations. According to domain experts, the developed system has big advantages for all tasks, which involve extensive data exploration.

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Acknowledgements

This work was funded by the Austrian Research Promotion Agency (FFG) under the scope of the COMET—Competence Centers for Excellent Technologies—program within the project “Knowledge Assisted Visual Fusion of Spatial Multi-Source Data (KAFus).”

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Authors and Affiliations

  1. VRVis Forschungs GmbH, Vienna, Austria

    M. Trapp, F. Schulze & K. Bühler

  2. Institute of Molecular Pathology, Vienna, Austria

    T. Liu & B. J. Dickson

Authors
  1. M. Trapp
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  2. F. Schulze
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  3. K. Bühler
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  4. T. Liu
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  5. B. J. Dickson
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Corresponding author

Correspondence to M. Trapp.

Appendix: Retrieval results

Appendix: Retrieval results

Table 4 Result images
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Trapp, M., Schulze, F., Bühler, K. et al. 3D object retrieval in an atlas of neuronal structures. Vis Comput 29, 1363–1373 (2013). https://doi.org/10.1007/s00371-013-0871-8

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