, Volume 9, Issue 2–3, pp 143–157

The DIADEM Data Sets: Representative Light Microscopy Images of Neuronal Morphology to Advance Automation of Digital Reconstructions

  • Kerry M. Brown
  • Germán Barrionuevo
  • Alison J. Canty
  • Vincenzo De Paola
  • Judith A. Hirsch
  • Gregory S. X. E. Jefferis
  • Ju Lu
  • Marjolein Snippe
  • Izumi Sugihara
  • Giorgio A. Ascoli


The comprehensive characterization of neuronal morphology requires tracing extensive axonal and dendritic arbors imaged with light microscopy into digital reconstructions. Considerable effort is ongoing to automate this greatly labor-intensive and currently rate-determining process. Experimental data in the form of manually traced digital reconstructions and corresponding image stacks play a vital role in developing increasingly more powerful reconstruction algorithms. The DIADEM challenge (short for DIgital reconstruction of Axonal and DEndritic Morphology) successfully stimulated progress in this area by utilizing six data set collections from different animal species, brain regions, neuron types, and visualization methods. The original research projects that provided these data are representative of the diverse scientific questions addressed in this field. At the same time, these data provide a benchmark for the types of demands automated software must meet to achieve the quality of manual reconstructions while minimizing human involvement. The DIADEM data underwent extensive curation, including quality control, metadata annotation, and format standardization, to focus the challenge on the most substantial technical obstacles. This data set package is now freely released ( to train, test, and aid development of automated reconstruction algorithms.


Axons Dendrites Neuroanatomy Tracing High-throughput Morphometry Optical imaging 


  1. Ascoli, G. A. (2008). Neuroinformatics grand challenges. Neuroinformatics, 6(1), 1–3.PubMedCrossRefGoogle Scholar
  2. Ascoli, G. A., Brown, K. M., Calixto, E., Card, J. P., Galván, E. J., Perez-Rosello, T., et al. (2009). Quantitative morphometry of electrophysiologically identified CA3b interneurons reveals robust local geometry and distinct cell classes. The Journal of Comparative Neurology, 515(6), 677–695.PubMedCrossRefGoogle Scholar
  3. Brown, K. M., Donohue, D. E., D’Alessandro, G., & Ascoli, G. A. (2005). A cross-platform freeware tool for digital reconstruction of neuronal arborizations from image stacks. Neuroinformatics, 3(4), 343–360.PubMedCrossRefGoogle Scholar
  4. Buckmaster, P. S., & Dudek, F. E. (1999). In vivo intracellular analysis of granule cell axon reorganization in epileptic rats. Journal of Neurophysiology, 81(2), 712–721.PubMedGoogle Scholar
  5. Calixto, E., Galván, E. J., Card, J. P., & Barrionuevo, G. (2008). Coincidence detection of convergent perforant path and mossy fibre inputs by CA3 interneurons. The Journal of Physiology, 586(Pt 11), 2695–2712.PubMedCrossRefGoogle Scholar
  6. Canty, A. J., & De Paola, V. (2011). Axonal reconstructions going live. Neuroinformatics. doi:10.1007/s12021-011-9112-3.
  7. De Paola, V., Arber, S., & Caroni, P. (2003). AMPA receptors regulate dynamic equilibrium of presynaptic terminals in mature hippocampal networks. Nature Neuroscience, 6(5), 491–500.PubMedGoogle Scholar
  8. De Paola, V., Holtmaat, A., Knott, G., Song, S., Wilbrecht, L., Caroni, P., et al. (2006). Cell type-specific structural plasticity of axonal branches and boutons in the adult neocortex. Neuron, 49(6), 861–875.PubMedCrossRefGoogle Scholar
  9. Donohue, D. E., & Ascoli, G. A. (2011). Automated reconstruction of neuronal morphology: an overview. Under review upon invitation, Brain Research Reviews.Google Scholar
  10. Evers, J. F., Schmitt, S., Sibila, M., & Duch, C. (2005). Progress in functional neuroanatomy: precise automatic geometric reconstruction of neuronal morphology from confocal image stacks. Journal of Neurophysiology, 93(4), 2331–2342.PubMedCrossRefGoogle Scholar
  11. Fares, T., & Stepanyants, A. (2009). Cooperative synapse formation in the neocortex. Proceedings of the National Academy of Sciences, 106(38), 16463–16468.CrossRefGoogle Scholar
  12. Feng, G., Mellor, R. H., Bernstein, M., Keller-Peck, C., Nguyen, Q. T., Wallace, M., Nerbonne, J. M., Lichtman, J. W., & Sanes, J. R. (2000). Imaging neuronal subsets in transgenic mice expressing multiple spectral variants of GFP. Neuron, 28(1), 41–51.Google Scholar
  13. Fiala, J. C. (2005). Reconstruct: a free editor for serial section microscopy. Journal of Microscopy, 218(Pt 1), 52–61.PubMedCrossRefGoogle Scholar
  14. Gao, Q., Yuan, B., & Chess, A. (2000). Convergent projections of Drosophila olfactory neurons to specific glomeruli in the antennal lobe. Nature Neuroscience, 3(8), 780–785.PubMedCrossRefGoogle Scholar
  15. Gilbert, C. D. (1983). Microcircuitry of the visual cortex. Annual Review of Neuroscience, 6, 217–247.PubMedCrossRefGoogle Scholar
  16. Gillette, T. A., Brown, K. M., & Ascoli, G. A. (2011). The DIADEM metric: comparing multiple reconstructions of the same neuron. Neuroinformatics. doi:10.1007/s12021-011-9117-y.
  17. Halavi, M., Polavaram, S., Donohue, D. E., Hamilton, G., Hoyt, J., Smith, K. P., et al. (2008). NeuroMorpho.Org implementation of digital neuroscience: dense coverage and integration with the NIF. Neuroinformatics, 6(3), 241–252.PubMedCrossRefGoogle Scholar
  18. Henneman, E., Somjen, G., & Carpenter, D. (1965). Functional significance of cell size in spinal motoneurons. Journal of Neurophysiology, 28(3), 560–580.PubMedGoogle Scholar
  19. Hirsch, J. A., Alonso, J. M., Reid, R. C., & Martinez, L. M. (1998a). Synaptic integration in striate cortical simple cells. The Journal of Neuroscience, 18(22), 9517–9528.Google Scholar
  20. Hirsch, J. A., Gallagher, C. A., Alonso, J. M., & Martinez, L. M. (1998b). Ascending projections of simple and complex cells in layer 6 of the cat striate cortex. The Journal of Neuroscience, 18(19), 8086–8094.Google Scholar
  21. Hirsch, J. A., Martinez, L. M., Pillai, C., Alonso, J. M., Wang, Q., & Sommer, F. T. (2003). Functionally distinct inhibitory neurons at the first stage of visual cortical processing. Nature Neuroscience, 6(12), 1300–1308.PubMedCrossRefGoogle Scholar
  22. Holtmaat, A., Bonhoeffer, T., Chow, D. K., Chuckowree, J., De Paola, V., Hofer, S. B., et al. (2009). Long-term, high-resolution imaging in the mouse neocortex through a chronic cranial window. Nature Protocols, 4(8), 1128–1144.PubMedCrossRefGoogle Scholar
  23. Jefferis, G. S. X. E., Potter, C. J., Chan, A. M., Marin, E. C., Rohlfing, T., Maurer, C. R., Jr., et al. (2007). Comprehensive maps of Drosophila higher olfactory centers: spatially segregated fruit and pheromone representation. Cell, 128(6), 1187–1203.PubMedCrossRefGoogle Scholar
  24. Kaspirzhny, A. V., Gogan, P., Horcholle-Bossavit, G., & Tyc-Dumont, S. (2002). Neuronal morphology data bases: morphological noise and assessment of data quality. Network: Computation in Neural Systems, 13, 357–380.CrossRefGoogle Scholar
  25. Lee, T., & Luo, L. (1999). Mosaic analysis with a repressible cell marker for studies of gene function in neuronal morphogenesis. Neuron, 22(3), 451–461.PubMedCrossRefGoogle Scholar
  26. Lichtman, J. W., Livet, J., & Sanes, J. R. (2008). A technicolour approach to the connectome. Nature Reviews Neuroscience, 9, 417–422.PubMedCrossRefGoogle Scholar
  27. Livet, J., Weissman, T. A., Kang, H., Draft, R. W., Lu, J., Bennis, R. A., et al. (2007). Transgenic strategies for combinatorial expression of fluorescent proteins in the nervous system. Nature, 450, 56–62.PubMedCrossRefGoogle Scholar
  28. Losavio, B. E., Liang, Y., Santamaria-Pang, A., Kakadiaris, I. A., Colbert, C. M., & Saggau, P. (2008). Live neuron morphology automatically reconstructed from multiphoton and confocal imaging data. Journal of Neurophysiology, 100, 2422–2429.PubMedCrossRefGoogle Scholar
  29. Lu, J. (2011). Neuronal tracing for connectomic studies. Neuroinformatics. doi:10.1007/s12021-011-9101-6.
  30. Lu, J., Fiala, J. C., & Lichtman, J. W. (2009a). Semi-automated reconstruction of neural processes from large numbers of fluorescence images. PLoS ONE, 4(5), e5655.CrossRefGoogle Scholar
  31. Lu, J., Tapia, J. C., White, O. L., & Lichtman, J. W. (2009b). The interscutularis muscle connectome. PLoS Biology, 7(2), e32. Erratum in: PLoS Biology, 7(4), e1000108.CrossRefGoogle Scholar
  32. Marin, E. C., Jefferis, G. S. X. E., Komiyama, T., Zhu, H., & Luo, L. (2002). Representation of the glomerular olfactory map in the Drosophila brain. Cell, 109(2), 243–255.PubMedCrossRefGoogle Scholar
  33. Martinez, L. M., Alonso, J. M., Reid, R. C., & Hirsch, J. A. (2002). Laminar processing of stimulus orientation in cat visual cortex. The Journal of Physiology, 540(Pt 1), 321–333.PubMedCrossRefGoogle Scholar
  34. Martinez, L. M., Wang, Q., Reid, R. C., Pillai, C., Alonso, J. M., Sommer, F. T., et al. (2005). Receptive field structure varies with layer in the primary visual cortex. Nature Neuroscience, 8(3), 372–379.PubMedCrossRefGoogle Scholar
  35. Meijering, E., Jacob, M., Sarria, J. C. F., Steiner, P., Hirling, H., & Unser, M. (2004). Design and validation of a tool for neurite tracing and analysis in fluorescence microscopy images. Cytometry, 58A(2), 167–176.CrossRefGoogle Scholar
  36. Peng, H., Ruan, Z., Atasoy, D., & Sternson, S. (2010). Automatic reconstruction of 3D neuron structures using a graph-augmented deformable model. Bioinformatics, 26(12), i38–i46.PubMedCrossRefGoogle Scholar
  37. Rodriguez, A., Ehlenberger, D. B., Hof, P. R., & Wearne, S. L. (2009). Three-dimensional neuron tracing by voxel scooping. Journal of Neuroscience Methods, 184(1), 169–175.PubMedCrossRefGoogle Scholar
  38. Scorcioni, R., Lazarewicz, M. T., & Ascoli, G. A. (2004). Quantitative morphometry of hippocampal pyramidal cells: differences between anatomical classes and reconstructing laboratories. The Journal of Comparative Neurology, 473(2), 177–193.PubMedCrossRefGoogle Scholar
  39. Scorcioni, R., Polavaram, S., & Ascoli, G. A. (2008). L-Measure: a web-accessible tool for the analysis, comparison and search of digital reconstructions of neuronal morphologies. Nature Protocols, 3(5), 866–876.PubMedCrossRefGoogle Scholar
  40. Snider, J., Pillaim, A., & Stevens, C. F. (2010). A universal property of axonal and dendritic arbors. Neuron, 66(1), 45–56.PubMedCrossRefGoogle Scholar
  41. Sugihara, I. (2011). Bright field neuronal preparation optimized for automatic computerized reconstruction, a case with cerebellar climbing fibers. Neuroinformatics. doi:10.1007/s12021-011-9099-9.
  42. Sugihara, I., Wu, H., & Shinoda, Y. (1999). Morphology of single olivocerebellar axons labeled with biotinylated dextran amine in the rat. The Journal of Comparative Neurology, 414(2), 131–148.PubMedCrossRefGoogle Scholar
  43. Torben-Nielsen, B., & Stiefel, K. M. (2009). Systematic mapping between dendritic function and structure. Network, 20(2), 69–105.PubMedCrossRefGoogle Scholar
  44. Volman, V., Levine, H., Ben-Jacob, E., & Sejnowski, T. J. (2009). Locally balanced dendritic integration by short-term synaptic plasticity and active dendritic conductances. Journal of Neurophysiology, 102(6), 3234–3250.PubMedCrossRefGoogle Scholar
  45. Vosshall, L. B., Wong, A. M., & Axel, R. (2000). An Olfactory Sensory Map in the Fly Brain. Cell, 102(2), 147–159.PubMedCrossRefGoogle Scholar
  46. Wittner, L., Henze, D. A., Záborszky, L., & Buzsáki, G. (2007). Three-dimensional reconstruction of the axon arbor of a CA3 pyramidal cell recorded and filled in vivo. Brain Structure and Function, 212(1), 75–83.PubMedCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Kerry M. Brown
    • 1
  • Germán Barrionuevo
    • 2
  • Alison J. Canty
    • 3
    • 8
  • Vincenzo De Paola
    • 3
  • Judith A. Hirsch
    • 4
  • Gregory S. X. E. Jefferis
    • 5
  • Ju Lu
    • 6
  • Marjolein Snippe
    • 3
    • 9
  • Izumi Sugihara
    • 7
  • Giorgio A. Ascoli
    • 1
  1. 1.Krasnow Institute for Advanced StudyGeorge Mason UniversityFairfaxUSA
  2. 2.Department of NeuroscienceUniversity of PittsburghPittsburghUSA
  3. 3.MRC Clinical Sciences CentreImperial College LondonLondonUK
  4. 4.Department of Biological SciencesUniversity of Southern CaliforniaLos AngelesUSA
  5. 5.Division of NeurobiologyMRC Laboratory of Molecular BiologyCambridgeUK
  6. 6.Department of Biological Sciences, James H. Clark Center for Biomedical Engineering and SciencesStanford UniversityStanfordUSA
  7. 7.Department of Systems NeurophysiologyTokyo Medical and Dental University School of MedicineTokyoJapan
  8. 8.School of MedicineUniversity of TasmaniaHobartAustralia
  9. 9.Blizard Institute of Cell and Molecular ScienceQueen Mary University of LondonLondonUK

Personalised recommendations