Neuroinformatics

, Volume 12, Issue 3, pp 423–434 | Cite as

Structure-Based Neuron Retrieval Across Drosophila Brains

  • Florian Ganglberger
  • Florian Schulze
  • Laszlo Tirian
  • Alexey Novikov
  • Barry Dickson
  • Katja Bühler
  • Georg Langs
Original Article

Abstract

Comparing local neural structures across large sets of examples is crucial when studying gene functions, and their effect in the Drosophila brain. The current practice of aligning brain volume data to a joint reference frame is based on the neuropil. However, even after alignment neurons exhibit residual location and shape variability that, together with image noise, hamper direct quantitative comparison and retrieval of similar structures on an intensity basis. In this paper, we propose and evaluate an image-based retrieval method for neurons, relying on local appearance, which can cope with spatial variability across the population. For an object of interest marked in a query case, the method ranks cases drawn from a large data set based on local neuron appearance in confocal microscopy data. The approach is based on capturing the orientation of neurons based on structure tensors and expanding this field via Gradient Vector Flow. During retrieval, the algorithm compares fields across cases, and calculates a corresponding ranking of most similar cases with regard to the local structure of interest. Experimental results demonstrate that the similarity measure and ranking mechanisms yield high precision and recall in realistic search scenarios.

Keywords

Drosophila Neuron retrieval Structure tensor Similarity measure Gradient vector flow 

References

  1. Birngruber, E., Langs, G., Donner, R. (2009). matVTK - 3D visualization for MATLAB. The MIDAS Journal, pp. 1–8.Google Scholar
  2. Brand, H., & Perrimon, N. (1993). Targeted gene expression as a means of altering cell fates and generating dominant phenotypes. Development (Cambridge, England), 118(2), 401–415.Google Scholar
  3. Briggman, K.L., & Denk, W. (2006). Towards neural circuit reconstruction with volume electron microscopy techniques. Current Opinion in Neurobiology, 16(5), 562–570.PubMedCrossRefGoogle Scholar
  4. Bruckner, S., Šoltészová, V., Gröller, M.E., Hladůvka, J., Bühler, K., Yu, J., Dickson, B.J. (2009). BrainGazer - Visual queries for neurobiology research. IEEE Transactions on Visualization and Computer Graphics, 15, 1497–1504.PubMedCrossRefGoogle Scholar
  5. Chigirev, D., & Bialek, W. (2004). Optimal manifold representation of data: an information theoretic approach. Advances in Neural Information Processing Systems, 16, 161.Google Scholar
  6. Dice, L.R. (1945). Measures of the amount of ecologic association between species. Ecology, 26(3), 297–302.CrossRefGoogle Scholar
  7. Dickson, B. (2012). Website of Dickson Group.Google Scholar
  8. Dittrich, E. (2009). Automatic segmentation of retinal vessels and measurement of doppler flow velocity in optical coherence tomography data. Diploma thesis, Vienna University of Technology.Google Scholar
  9. Durrleman, S., Fillard, P., Pennec, X., Trouvé, A., Ayache, N. (2011). Registration, atlas estimation and variability analysis of white matter fiber bundles modeled as currents. NeuroImage, 55(3), 1073–1090.PubMedCrossRefGoogle Scholar
  10. Frangi, A.F., Niessen, W.J., Vincken, K.L., Viergever, M.A. (1998). Multiscale vessel enhancement filtering. Medical image computing and computer-assisted intervention: MICCAI... International Conference on Medical Image Computing and Computer-Assisted Intervention, 1496, 130–137.Google Scholar
  11. Gillette, T.a., Brown, K.M., Ascoli, G.a. (2011). The DIADEM metric: comparing multiple reconstructions of the same neuron. Neuroinformatics, 9(2–3), 233–245.PubMedCrossRefGoogle Scholar
  12. Hassouna, M.S., & Farag, A.a. (2009). Variational curve skeletons using gradient vector flow. IEEE Transactions on Pattern Analysis and Machine Intelligence, 31(12), 2257–2274.PubMedCrossRefGoogle Scholar
  13. Jefferis, G.S.X.E., Potter, C.J., Chan, A.M., Marin, E.C., Rohlfing, T., Maurer, C.R., Luo, L. (2007). Comprehensive maps of drosophila higher olfactory centers: spatially segregated fruit and pheromone representation. Cell, 128(6), 1187–1203.PubMedCentralPubMedCrossRefGoogle Scholar
  14. Kennedy, D.N., Hodge, S.M., Gao, Y., Frazier, J.a., Haselgrove, C. (2012). The internet brain volume database: a public resource for storage and retrieval of volumetric data. Neuroinformatics, 10(2), 129–140.PubMedCentralPubMedCrossRefGoogle Scholar
  15. Kimura, K.-I., Hachiya, T., Koganezawa, M., Tazawa, T., Yamamoto, D. (2008). Fruitless and doublesex coordinate to generate malespecific neurons that can initiate courtship. Neuron, 59(5), 759–769.PubMedCrossRefGoogle Scholar
  16. Knutsson, H. (1989). Representing local structure using tensors. The 6th Scandinavian conference on image analysis (pp. 244–251).Google Scholar
  17. Knutsson, H., Westin, C., Andersson, M. (2011). Representing local structure using tensors II. Image Analysis Lecture Notes in Computer Science, 6688, 545–556. http://link.springer.com/chapter/10.1007%2F978-3-642-21227-7_51
  18. Masse, N.Y., Cachero, S., Ostrovsky, A.D., Jefferis, G.S.X.E. (2012). A mutual information approach to automate identification of neuronal clusters in Drosophila brain images. Frontiers in Neuroinformatics, 6, 21.PubMedCentralPubMedCrossRefGoogle Scholar
  19. Nicolaï, L.J.J., Ramaekers, A., Raemaekers, T., Drozdzecki, A., Mauss, A.S., Yan, J., Landgraf, M., Annaert, W., Hassan, B.A. (2010). Genetically encoded dendritic marker sheds light on neuronal connectivity in Drosophila. Proceedings of the National Academy of Sciences of the United States of America, 107(47), 20553–20558.PubMedCentralPubMedCrossRefGoogle Scholar
  20. Otsu, N. (1979). A threshold selection method from gray-level histograms. IEEE Transactions on Systems Man and Cybernetics, 9(1), 62–66.CrossRefGoogle Scholar
  21. Rohlfing, T., & Maurer, C.R. (2003). Nonrigid image registration in shared-memory multiprocessor environments with application to brains, breasts, and bees. IEEE Transactions on Information Technology in Biomedicine: A Publication of the IEEE Engineering in Medicine and Biology Society, 7(1), 16–25.CrossRefGoogle Scholar
  22. Sato, Y., Nakajima, S., Shiraga, N., Atsumi, H., Yoshida, S., Koller, T., Gerig, G., Kikinis, R. (1998). 3D multi-scale line filter for segmentation and visualization of curvilinear structures in medical images. Medical Image Analysis, 2(2), 143–168.PubMedCrossRefGoogle Scholar
  23. Shapiro, L., & Stockman, G. (2002). Computer vision. Texts in computer science. Prentice Hall.Google Scholar
  24. Sokolowski, M.B. (2001). Drosophila: genetics meets behaviour. Nature Reviews Genetics, 2(11), 879–890.PubMedCrossRefGoogle Scholar
  25. Stalling, D., Westerhoff, M., Hege, H.-C. (2005). Amira: a highly interactive system for visual data analysis.Google Scholar
  26. Stockinger, P., Kvitsiani, D., Rotkopf, S., Tirián, L., Dickson, B.J. (2005). Neural circuitry that governs Drosophila male courtship behavior. Cell, 121(5), 795–807.PubMedCrossRefGoogle Scholar
  27. Tenenbaum, J.B., de Silva, V., Langford, J.C. (2000). A global geometric framework for nonlinear dimensionality reduction. Science (New York, N.Y.), 290(5500), 2319–2323.CrossRefGoogle Scholar
  28. Verma, R., Khurd, P., Davatzikos, C. (2007). On analyzing diffusion tensor images by identifying manifold structure using isomaps. IEEE Transactions on Medical Imaging, 26(6), 772–778.PubMedCrossRefGoogle Scholar
  29. Von Philipsborn, A.C., Liu, T., Yu, J.Y., Masser, C., Bidaye, S.S., Dickson, B.J. (2011). Neuronal control of Drosophila courtship song. Neuron, 69(3), 509–522.PubMedCrossRefGoogle Scholar
  30. Xu, C., & Prince, J.L. (1998). Snakes, shapes, and gradient vector flow. IEEE Transactions on Image Processing: A Publication of the IEEE Signal Processing Society, 7(3), 359–369.CrossRefGoogle Scholar
  31. Yang, J., Gonzalez-Bellido, P.T., Peng, H. (2013). A distance-field based automatic neuron tracing method. BMC Bioinformatics, 14, 93.PubMedCentralPubMedCrossRefGoogle Scholar
  32. Yu, J.Y., Kanai, M.I., Demir, E., Jefferis, G.S.X.E., Dickson, B.J. (2010). Cellular organization of the neural circuit that drives Drosophila courtship behavior. Current Biology, 20(18), 1602–1614.PubMedCrossRefGoogle Scholar
  33. Zhang, K. (1996). A constrained edit distance between unordered labeled trees. Algorithmica, 15(3), 205–222.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  • Florian Ganglberger
    • 1
    • 2
  • Florian Schulze
    • 2
  • Laszlo Tirian
    • 3
  • Alexey Novikov
    • 2
  • Barry Dickson
    • 3
  • Katja Bühler
    • 2
  • Georg Langs
    • 1
  1. 1.CIR Lab, Department of Biomedical Imaging and Image-guided TherapyMedical University of ViennaViennaAustria
  2. 2.VRVis Research CenterViennaAustria
  3. 3.Dickson Group, IMP - Research Institute of Molecular PathologyViennaAustria

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