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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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References

  1. Blankenship, J.E., Houck, B.: Nervous system (invertebrate) (2012). http://accessscience.com/content/Nervous-system-(invertebrate)/449210

  2. Bronstein, A., Bronstein, M., Guibas, L., Ovsjanikov, M.: Shape Google: geometric words and expressions for invariant shape retrieval. ACM Trans. Graph. 30(1) (2011)

  3. Bruckner, S., Soltészová, V., Gröller, E., Hladuvka, J., Bühler, K., Yu, J.Y., Dickson, B.: BrainGazer-visual queries for neurobiology research. IEEE Trans. Vis. Comput. Graph. 15(6), 1497–1504 (2009)

    Article  Google Scholar 

  4. Cardona, A., Saalfeld, S., Arganda, I., Pereanu, W., Schindelin, J., Hartenstein, V.: Identifying neuronal lineages of Drosophila by sequence analysis of axon tracts. J. Neurosci. 30(22), 7538–7553 (2010)

    Article  Google Scholar 

  5. Demiralp, C., Laidlaw, D.: Similarity coloring of DTI fiber tracts. In: Proceedings of DMFC Workshop at MICCAI (2009)

    Google Scholar 

  6. Fehr, J., Streicher, A., Burkhardt, H.: A bag of features approach for 3D shape retrieval. In: Advances in Visual Computing, vol. 5875, pp. 34–43 (2009)

    Chapter  Google Scholar 

  7. Grauman, K., Darrell, T.: The pyramid match kernel: discriminative classification with sets of image features. In: Proceedings of the IEEE International Conference on Computer Vision, October, pp. 1458–1465. IEEE Press, New York (2005)

    Google Scholar 

  8. Güntzer, U., Balke, W., Kiessing, W.: Optimizing multi-feature queries for image databases. In: Proceedings of the International Conference on Very Large Data Bases, pp. 419–428. Morgan Kaufmann, San Mateo (2000)

    Google Scholar 

  9. Ilyas, I.F., Beskales, G., Soliman, M.A.: A survey of top-k query processing techniques in relational database systems. ACM Comput. Surv. 40(4), 11 (2008)

    Article  Google Scholar 

  10. Jiang, X., Bunke, H., Abegglen, K., Kandel, A.: Curve morphing by weighted mean of strings. In: Proceedings of the International Conference on Pattern Recognition, vol. 4, pp. 192–195. IEEE Press, New York (2002)

    Google Scholar 

  11. Lee, P., Ching, Y., Chang, H.: A semi-automatic method for neuron centerline extraction in confocal microscopic image stack. In: Proceedings of the IEEE International Symposium on Biomedical Imaging, pp. 959–962 (2008)

    Google Scholar 

  12. Li, X., Godil, A.: Exploring the bag-of-words method for 3D shape retrieval. In: Proceedings of the IEEE International Conference on Image Processing, pp. 437–440 (2009)

    Google Scholar 

  13. Lian, Z., Godil, A., Bustos, B., Daoudi, M., Hermans, J., Kawamura, S., Kurita, Y., Lavoué, G., Van Nguyen, H., Ohbuchi, R., Ohkita, Y., Ohishi, Y., Porikli, F., Reuter, F., Sipiran, I., Smeets, D., Suetens, P., Tabia, H., Vandermeulen, D.: A comparison of methods for non-rigid 3D shape retrieval. Pattern Recognit. 46, 449–461 (2012)

    Article  Google Scholar 

  14. Lin, C.Y., Tsai, K.L., Wang, S.C., Hsieh, C.H., Chang, H.M., Chiang, A.S.: The neuron navigator: exploring the information pathway through the neural maze. In: Proceedings of the Pacific Visualization Symposium, pp. 35–42. IEEE Press, New York (2011)

    Google Scholar 

  15. Lu, G., Sajjanhar, A.: Region-based shape representation and similarity measure suitable for content-based image retrieval. Multimed. Syst. 7(2), 165–174 (1999)

    Article  Google Scholar 

  16. Moberts, B., Vilanova, A., van Wijk, J.J.: Evaluation of fiber clustering methods for diffusion tensor imaging. In: Proceedings of the IEEE Visualization, pp. 65–72 (2005)

    Google Scholar 

  17. Mori, G., Belongie, S., Malik, J.: Efficient shape matching using shape contexts. In: Proceedings of the IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, pp. 1832–1837 (2005)

    Google Scholar 

  18. NCHC, BRC/NTHU: Fly Circuit (2012). http://www.flycircuit.tw/

  19. Ohbuchi, R., Osada, K., Furuya, T., Banno, T.: Salient local visual features for shape-based 3D model retrieval. In: Proceedings of IEEE International Conference on Shape Modeling and Applications, pp. 93–102 (2008)

    Google Scholar 

  20. Olsen, S.R., Wilson, R.I.: Cracking neural circuits in a tiny brain: new approaches for understanding the neural circuitry of Drosophila. Trends Neurosci. 31(10), 512–520 (2008)

    Article  Google Scholar 

  21. Peng, H.: Bioimage informatics: a new area of engineering biology. Bioinformatics 24(17), 1827–1836 (2008)

    Article  Google Scholar 

  22. Peng, H., Chung, P., Long, F., Qu, L., Jenett, A., Seeds, A.M., Myers, E.W., Simpson, J.H.: BrainAligner: 3D registration atlases of Drosophila brains. Nat. Methods 8(6), 493–500 (2011)

    Article  Google Scholar 

  23. Rohlfing, T., Maurer, C.R.: Nonrigid image registration in shared-memory multiprocessor environments with application to brains, breasts, and bees. IEEE Trans. Inf. Technol. Biomed. 7(1), 16–25 (2003)

    Article  Google Scholar 

  24. Scorcioni, R., Polavaram, S., Ascoli, G.A.: L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies. Nat. Protoc. 3(5), 866–876 (2008)

    Article  Google Scholar 

  25. Sfikas, K., Theoharis, T., Pratikakis, I.: Non-rigid 3D object retrieval using topological information guided by conformal factors. Vis. Comput. 28, 943–955 (2012)

    Article  Google Scholar 

  26. Sherbondy, A., Akers, D., Mackenzie, R., Dougherty, R., Wandell, B.: Exploring Connectivity of the Brain ’ s White Matter with Dynamic Queries. IEEE Trans. Vis. Comput. Graph. 11(4), 419–430 (2005)

    Article  Google Scholar 

  27. Tangelder, J.W.H., Veltkamp, R.C.: A survey of content based 3D shape retrieval methods. Multimed. Tools Appl. 39(3), 441–471 (2007)

    Article  Google Scholar 

  28. Van Essen, D.C.: Windows on the brain: the emerging role of atlases and databases in neuroscience. Curr. Opin. Neurobiol. 12(5), 574–579 (2002)

    Article  Google Scholar 

  29. Wang, X., Liu, Y., Zha, H.: Intrinsic spin images: a subspace decomposition approach to understanding 3D deformable shapes. In: Proceedings of the Fifth International Symposium 3D Data Processing, Visualization and Transmission (2010)

    Google Scholar 

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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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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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