, 9:233

The DIADEM Metric: Comparing Multiple Reconstructions of the Same Neuron

  • Todd A. Gillette
  • Kerry M. Brown
  • Giorgio A. Ascoli
Original Article


Digital reconstructions of neuronal morphology are used to study neuron function, development, and responses to various conditions. Although many measures exist to analyze differences between neurons, none is particularly suitable to compare the same arborizing structure over time (morphological change) or reconstructed by different people and/or software (morphological error). The metric introduced for the DIADEM (DIgital reconstruction of Axonal and DEndritic Morphology) Challenge quantifies the similarity between two reconstructions of the same neuron by matching the locations of bifurcations and terminations as well as their topology between the two reconstructed arbors. The DIADEM metric was specifically designed to capture the most critical aspects in automating neuronal reconstructions, and can function in feedback loops during algorithm development. During the Challenge, the metric scored the automated reconstructions of best-performing algorithms against manually traced gold standards over a representative data set collection. The metric was compared with direct quality assessments by neuronal reconstruction experts and with clocked human tracing time saved by automation. The results indicate that relevant morphological features were properly quantified in spite of subjectivity in the underlying image data and varying research goals. The DIADEM metric is freely released open source ( as a flexible instrument to measure morphological error or change in high-throughput reconstruction projects.


Algorithm Automation Axon Computational neuroanatomy Dendrite Digital tracing Morphology Optical imaging 


  1. Ascoli, G. A. (2002). Neuroanatomical algorithms for dendritic modelling. Network, 13, 247–260.PubMedCrossRefGoogle Scholar
  2. Ascoli, G. A., Alonso-Nanclares, L., Anderson, S. A., Barrionuevo, G., Benavides-Piccione, R., Burkhalter, A., et al. (2008). Petilla terminology: nomenclature of features of GABAergic interneurons of the cerebral cortex. Nature Reviews Neuroscience, 9, 557–568.PubMedCrossRefGoogle Scholar
  3. Baloyannis, S. J. (2009). Dendritic pathology in Alzheimer’s disease. Journal of the Neurological Sciences, 283, 153–157.PubMedCrossRefGoogle Scholar
  4. Binzegger, T., Douglas, R. J., & Martin, K. A. (2004). A quantitative map of the circuit of cat primary visual cortex. Journal of Neuroscience, 24, 8441–8453.PubMedCrossRefGoogle Scholar
  5. 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, 343–359.PubMedCrossRefGoogle Scholar
  6. Brown, K. M., Gillette, T. A., & Ascoli, G. A. (2008). Quantifying neuronal size: summing up trees and splitting the branch difference. Seminars in Cell & Developmental Biology, 19, 485–493.CrossRefGoogle Scholar
  7. Brown, K. M., Barrionuevo, G., Canty, A. J., De Paola, V., Hirsch, J. A., Jefferis, G. S. X. E., et al. (2011) The DIADEM data sets: representative light microscopy images of neuronal morphology to advance automation of digital reconstructions. Neuroinformatics, doi:10.1007/s12021-010-9095-5.
  8. Bülow, T., Lorenz, C., Wiemker, R., & Honko, J. (2006). Point based methods for automatic bronchial tree matching and labeling. Proceedings of the SPIE, 7, 225–234.Google Scholar
  9. Canty, A. J. & De Paola, V. (2011) Axonal reconstructions going live. Neuroinformatics, doi:10.1007/s12021-011-9112-3.
  10. Capowski, J. J. (1983). An automated neuron reconstruction system. Journal of Neuroscience Methods, 8, 353–364.PubMedCrossRefGoogle Scholar
  11. Cardona, A., Saalfeld, S., Arganda, I., Pereanu, W., Schindelin, J., & Hartenstein, V. (2010). Identifying neuronal lineages of Drosophila by sequence analysis of axon tracts. Journal of Neuroscience, 30, 7538–7553.PubMedCrossRefGoogle Scholar
  12. Charnoz, A., Agnus, V., Malandain, G., Soler, L., & Tajine, M. (2005). Tree matching applied to vascular system. In L. Brun & M. Vento (Eds.), Graph-based representations in pattern recognition (pp. 183–192). Berlin: Springer.CrossRefGoogle Scholar
  13. Chklovskii, D. B., Vitaladevuni, S., & Scheffer, L. K. (2010). Semi-automated reconstruction of neural circuits using electron microscopy. Current Opinion in Neurobiology, 20, 667–675.PubMedCrossRefGoogle Scholar
  14. Cline, H. (2001). Dendritic arbor development and synaptogenesis. Current Opinion in Neurobiology, 11, 118–126.PubMedCrossRefGoogle Scholar
  15. Cuntz, H., Forstner, F., Borst, A., & Häusser, M. (2011). The TREES toolbox – probing the basis of axonal and dendritic branching. Neuroinformatics, in press.Google Scholar
  16. Drechsler, K., Laura, C. O., Chen, Y., & Erdt, M. (2010). Semi-automatic anatomical tree matching for landmark-based elastic registration of liver volumes. Journal of Healthcare Engineering, 1, 101–124.CrossRefGoogle Scholar
  17. Gillette, T. A., & Grefenstette, J. J. (2009). On comparing neuronal morphologies with the constrained tree-edit-distance. Neuroinformatics, 7, 191–194.PubMedCrossRefGoogle Scholar
  18. Glaser, E. M., & Van der Loos, H. (1965). A semi-automatic computer microscope for the analysis of neuronal morphology. IEEE Transactions on Biomedical Engineering, 12, 22–40.PubMedCrossRefGoogle Scholar
  19. Glaser, J. R., & Glaser, E. M. (1990). Neuron imaging with Neurolucida—a PC-based system for image combining microscopy. Computerized Medical Imaging and Graphics, 14, 307–317.PubMedCrossRefGoogle Scholar
  20. Goldberg, J., Hamzei-Sichani, F., MacLean, J., Tamas, G., Urban, R., & Yuste, R. (2006). From dendrites to networks: optically probing the living brain slice and using principal component analysis to characterize neuronal morphology. In L. Zaborszky, F. G. Wouterlood, & J. L. Lanciego (Eds.), Neuroanatomical tract-tracing 3: Molecules, neurons, and systems (pp. 452–476). US: Springer.CrossRefGoogle Scholar
  21. Hao, H., & Shreiber, D. I. (2007). Axon kinematics change during growth and development. Journal of Biomechanical Engineering, 129, 511–522.PubMedCrossRefGoogle Scholar
  22. Haug, H. (1987). Brain sizes, surfaces, and neuronal sizes of the cortex cerebri: a stereological investigation of man and his variability and a comparison with some mammals (primates, whales, marsupials, insectivores, and one elephant). American Journal of Anatomy, 180, 126–142.PubMedCrossRefGoogle Scholar
  23. Heumann, H., & Wittum, G. (2009). The tree-edit-distance, a measure for quantifying neuronal morphology. Neuroinformatics, 7, 179–190.PubMedCrossRefGoogle Scholar
  24. Jaeger, D. (2001) Accurate reconstruction of neuronal morphology. In E. de Schutter (ed.), Computational neuroscience: Realistic modeling for experimentalists. CRC Press, pp. 159–178.Google Scholar
  25. Kaspirzhny, A. V., Gogan, P., Horcholle-Bossavit, G., & Tyc-Dumont, S. (2002). Neuronal morphology data bases: morphological noise and assesment of data quality. Network, 13, 357–380.PubMedCrossRefGoogle Scholar
  26. Kasthuri, N., & Lichtman, J. W. (2010). Neurocartography. Neuropsychopharmacology, 35, 342–343.PubMedCrossRefGoogle Scholar
  27. Koene, R. A., Tijms, B., van Hees, P., Postma, F., de Ridder, A., Ramakers, G. J., et al. (2009). NETMORPH: a framework for the stochastic generation of large scale neuronal networks with realistic neuron morphologies. Neuroinformatics, 7, 195–210.PubMedCrossRefGoogle Scholar
  28. Krichmar, J. L., Nasuto, S. J., Scorcioni, R., Washington, S. D., & Ascoli, G. A. (2002). Effects of dendritic morphology on CA3 pyramidal cell electrophysiology: a simulation study. Brain Research, 941, 11–28.PubMedCrossRefGoogle Scholar
  29. Li, Y., Brewer, D., Burke, R. E., & Ascoli, G. A. (2005). Developmental changes in spinal motoneuron dendrites in neonatal mice. Journal of Comparative Neurology, 483, 304–317.PubMedCrossRefGoogle Scholar
  30. Lin, B., & Masland, R. H. (2005). Synaptic contacts between an identified type of ON cone bipolar cell and ganglion cells in the mouse retina. The European Journal of Neuroscience, 21, 1257–1270.PubMedCrossRefGoogle Scholar
  31. Losavio, B. E., Liang, Y., Santamaría-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
  32. Lu, J., Tapia, J. C., White, O. L., & Lichtman, J. W. (2009). The interscutularis muscle connectome. PLoS Biology, 7, e1000032.Google Scholar
  33. Luisi, J., Narayanaswamy, A., Galbreath, Z., & Roysam, B. (2011). The FARSIGHT Trace Editor: An Open Source Tool for 3-D Inspection and Efficient Pattern Analysis Aided Editing of Automated Neuronal Reconstructions. Neuroinformatics, doi:10.1007/s12021-011-9115-0.
  34. Mainen, Z., & Sejnowski, T. (1996). Influence of dendritic structure on firing pattern in model neocortical neurons. Nature, 382, 363–366.PubMedCrossRefGoogle Scholar
  35. Markram, H., Toledo-Rodriguez, M., Wang, Y., Gupta, A., Silberberg, G., & Wu, C. (2004). Interneurons of the neocortical inhibitory system. Nature Reviews Neuroscience, 5, 793–807.PubMedCrossRefGoogle Scholar
  36. Marks, W. B., & Burke, R. E. (2007). Simulation of motoneuron morphology in three dimensions. I. Building individual dendritic trees. The Journal of Comparative Neurology, 503, 685–700.PubMedCrossRefGoogle Scholar
  37. Metzen, J. H., Kröger, T., Schenk, A., Zidowitz, S., Peitgen, H., & Jiang, X. (2009). Matching of anatomical tree structures for registration of medical images. Image and Vision Computing, 27, 923–933.CrossRefGoogle Scholar
  38. Meyer-Luehmann, M., Spires-Jones, T. L., Prada, C., Garcia-Alloza, M., de Calignon, A., Rozkalne, A., et al. (2008). Rapid appearance and local toxicity of amyloid-beta plaques in a mouse model of Alzheimer’s disease. Nature, 451, 720–724.PubMedCrossRefGoogle Scholar
  39. Mize, R. R. (1984). Computer applications in cell and neurobiology: a review. International Review of Cytology, 90, 83–124.PubMedCrossRefGoogle Scholar
  40. Overdijk, J., Uylings, H. B. M., Kuypers, K., & Kamstra, A. W. (1978). An economical semi-automatic system for measuring cellular tree structures in three dimensions, with special emphasis on Golgi-impregnated neurons. Journal of Microscopy, 114, 271–284.PubMedGoogle Scholar
  41. Peng, H., Ruan, Z., Long, F., Simpson, J. H., & Myers, E. W. (2010). V3D enables real-time 3D visualization and quantitative analysis of large-scale biological image data sets. Nature Biotechnology, 28, 348–353.PubMedCrossRefGoogle Scholar
  42. Peng, H., Ruan, Z., Atasoy, D., & Sternson, S. (2010). Automatic reconstruction of 3D neuron structures using a graph-augmented deformable model. Bioinformatics, 26, i38–i46.PubMedCrossRefGoogle Scholar
  43. Peng, H., Long, F., Zhao, T., & Myers, E. (2011). Proof-editing is the bottleneck of 3D neuron reconstruction: the problem and solutions. Neuroinformatics, doi:10.1007/s12021-010-9090-x.
  44. Rodriguez, A., Ehlenberger, D. B., Hof, P. R., & Wearne, S. L. (2009). Three-dimensional neuron tracing by voxel scooping. Journal of Neuroscience Methods, 184, 169–175.PubMedCrossRefGoogle Scholar
  45. Schaap, M., Metz, C. T., van Walsum, T., van Der Giessen, A. G., Weustink, A. C., Mollet, N. R., et al. (2009). Standardized evaluation methodology and reference database for evaluating coronary artery centerline extraction algorithms. Medical Image Analysis, 13, 701–714.PubMedCrossRefGoogle Scholar
  46. Schaefer, A. T., Larkum, M. E., Sakmann, B., & Roth, A. (2003). Coincidence detection in pyramidal neurons is tuned by their dendritic branching pattern. Journal of Neurophysiology, 89, 3143–3154.PubMedCrossRefGoogle Scholar
  47. 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, 177–93.PubMedCrossRefGoogle Scholar
  48. Senft, S. L. (2011). A brief history of neuronal reconstruction. Neuroinformatics , doi:10.1007/s12021-011-9107-0.
  49. Stepanyants, A., & Chklovskii, D. (2005). Neurogeometry and potential synaptic connectivity. Trends in Neuroscience, 28, 387–394.CrossRefGoogle Scholar
  50. Stepanyants, A., Tamás, G., & Chklovskii, D. B. (2004). Class-specific features of neuronal wiring. Neuron, 43, 251–259.PubMedCrossRefGoogle Scholar
  51. Sugihara, I., Wu, H., & Shinoda, Y. (1996). Morphology of axon collaterals of single climbing fibers in the deep cerebellar nuclei of the rat. Neuroscience Letters, 217, 33–36.PubMedCrossRefGoogle Scholar
  52. Tschirren, J., McLennan, G., Palágyi, K., Hoffman, E. A., & Sonka, M. (2005). Matching and anatomical labeling of human airway tree. IEEE Transactions on Medical Imaging, 24, 1540–1547.PubMedCrossRefGoogle Scholar
  53. Tyrrell, J. A., di Tomaso, E., Fuja, D., Tong, R., Kozak, K., Jain, R. K., et al. (2007). Robust 3-D modeling of vasculature imagery using superellipsoids. IEEE Transactions on Medical Imaging, 26, 223–237.PubMedCrossRefGoogle Scholar
  54. Van Ooyen, A., Duijnhouwer, J., Remme, M., & van Pelt, J. (2002). The effect of dendritic topology on firing patterns in model neurons. Network: Computation in Neural Systems, 13, 311–325.CrossRefGoogle Scholar
  55. Van Pelt, J., Uylings, H. B. M., Verwer, R. W. H., Pentney, R. J., & Woldenberg, M. J. (1992). Tree asymmetry—a sensitive and practical measure for binary topological trees. Bulletin of Mathematical Biology, 54(5), 759–784.PubMedCrossRefGoogle Scholar
  56. van Praag, H., Kempermann, G., & Gage, F. H. (2000). Neural consequences of environmental enrichment. Nature Reviews Neuroscience, 1, 191–198.PubMedCrossRefGoogle Scholar
  57. Vetter, P., Roth, A., & Häusser, M. (2001). Propagation of action potentials in dendrites depends on dendritic morphology. Journal of Neurophysiology, 85, 926–937.PubMedGoogle Scholar
  58. Wearne, S. L., Rodriguez, A., Ehlenberger, D. B., Rocher, A. B., Henderson, S. C., & Hof, P. R. (2005). New techniques for imaging, digitization and analysis of three-dimensional neural morphology on multiple scales. Neuroscience, 136, 661–680.PubMedCrossRefGoogle Scholar
  59. Wong, R. O., & Ghosh, A. (2002). Activity-dependent regulation of dendritic growth and patterning. Nature Reviews Neuroscience, 3, 803–812.PubMedCrossRefGoogle Scholar
  60. Zhang, K. (1996). A constrained edit distance between unordered labeled trees. Algorithmica, 15, 205–222.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Todd A. Gillette
    • 1
  • Kerry M. Brown
    • 1
  • Giorgio A. Ascoli
    • 1
  1. 1.Center for Neural Informatics, Structures, & Plasticity, and Molecular Neuroscience Department, Krasnow Institute for Advanced StudyMS2A1 George Mason UniversityFairfaxUSA

Personalised recommendations