Skip to main content

Advertisement

Log in

The DIADEM Metric: Comparing Multiple Reconstructions of the Same Neuron

  • Original Article
  • Published:
Neuroinformatics Aims and scope Submit manuscript

Abstract

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 (http://diademchallenge.org) as a flexible instrument to measure morphological error or change in high-throughput reconstruction projects.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9

Similar content being viewed by others

Notes

  1. http://www.neuronland.org/NLMorphologyConverter/MorphologyFormats/SWC/Spec.html

References

  • Ascoli, G. A. (2002). Neuroanatomical algorithms for dendritic modelling. Network, 13, 247–260.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • Baloyannis, S. J. (2009). Dendritic pathology in Alzheimer’s disease. Journal of the Neurological Sciences, 283, 153–157.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

  • 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 

  • Canty, A. J. & De Paola, V. (2011) Axonal reconstructions going live. Neuroinformatics, doi:10.1007/s12021-011-9112-3.

  • Capowski, J. J. (1983). An automated neuron reconstruction system. Journal of Neuroscience Methods, 8, 353–364.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • Cline, H. (2001). Dendritic arbor development and synaptogenesis. Current Opinion in Neurobiology, 11, 118–126.

    Article  PubMed  CAS  Google Scholar 

  • Cuntz, H., Forstner, F., Borst, A., & Häusser, M. (2011). The TREES toolbox – probing the basis of axonal and dendritic branching. Neuroinformatics, in press.

  • 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.

    Article  Google Scholar 

  • Gillette, T. A., & Grefenstette, J. J. (2009). On comparing neuronal morphologies with the constrained tree-edit-distance. Neuroinformatics, 7, 191–194.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Chapter  Google Scholar 

  • Hao, H., & Shreiber, D. I. (2007). Axon kinematics change during growth and development. Journal of Biomechanical Engineering, 129, 511–522.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • Heumann, H., & Wittum, G. (2009). The tree-edit-distance, a measure for quantifying neuronal morphology. Neuroinformatics, 7, 179–190.

    Article  PubMed  Google Scholar 

  • Jaeger, D. (2001) Accurate reconstruction of neuronal morphology. In E. de Schutter (ed.), Computational neuroscience: Realistic modeling for experimentalists. CRC Press, pp. 159–178.

  • 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.

    Article  PubMed  Google Scholar 

  • Kasthuri, N., & Lichtman, J. W. (2010). Neurocartography. Neuropsychopharmacology, 35, 342–343.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • Lu, J., Tapia, J. C., White, O. L., & Lichtman, J. W. (2009). The interscutularis muscle connectome. PLoS Biology, 7, e1000032.

    Google Scholar 

  • 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.

  • Mainen, Z., & Sejnowski, T. (1996). Influence of dendritic structure on firing pattern in model neocortical neurons. Nature, 382, 363–366.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • Mize, R. R. (1984). Computer applications in cell and neurobiology: a review. International Review of Cytology, 90, 83–124.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    PubMed  CAS  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • 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.

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • Senft, S. L. (2011). A brief history of neuronal reconstruction. Neuroinformatics , doi:10.1007/s12021-011-9107-0.

  • Stepanyants, A., & Chklovskii, D. (2005). Neurogeometry and potential synaptic connectivity. Trends in Neuroscience, 28, 387–394.

    Article  CAS  Google Scholar 

  • Stepanyants, A., Tamás, G., & Chklovskii, D. B. (2004). Class-specific features of neuronal wiring. Neuron, 43, 251–259.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • van Praag, H., Kempermann, G., & Gage, F. H. (2000). Neural consequences of environmental enrichment. Nature Reviews Neuroscience, 1, 191–198.

    Article  PubMed  Google Scholar 

  • Vetter, P., Roth, A., & Häusser, M. (2001). Propagation of action potentials in dendrites depends on dendritic morphology. Journal of Neurophysiology, 85, 926–937.

    PubMed  CAS  Google Scholar 

  • 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.

    Article  PubMed  CAS  Google Scholar 

  • Wong, R. O., & Ghosh, A. (2002). Activity-dependent regulation of dendritic growth and patterning. Nature Reviews Neuroscience, 3, 803–812.

    Article  PubMed  CAS  Google Scholar 

  • Zhang, K. (1996). A constrained edit distance between unordered labeled trees. Algorithmica, 15, 205–222.

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We are grateful to Dr. Karel Svoboda for early discussions on the development of the DIADEM metric. This work was supported in part by HHMI and NIH grant R01NS39600.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Giorgio A. Ascoli.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Gillette, T.A., Brown, K.M. & Ascoli, G.A. The DIADEM Metric: Comparing Multiple Reconstructions of the Same Neuron. Neuroinform 9, 233–245 (2011). https://doi.org/10.1007/s12021-011-9117-y

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12021-011-9117-y

Keywords

Navigation