, Volume 9, Issue 2, pp 143–157

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


  • Kerry M. Brown
    • Krasnow Institute for Advanced StudyGeorge Mason University
  • Germán Barrionuevo
    • Department of NeuroscienceUniversity of Pittsburgh
  • Alison J. Canty
    • MRC Clinical Sciences CentreImperial College London
    • School of MedicineUniversity of Tasmania
  • Vincenzo De Paola
    • MRC Clinical Sciences CentreImperial College London
  • Judith A. Hirsch
    • Department of Biological SciencesUniversity of Southern California
  • Gregory S. X. E. Jefferis
    • Division of NeurobiologyMRC Laboratory of Molecular Biology
  • Ju Lu
    • Department of Biological Sciences, James H. Clark Center for Biomedical Engineering and SciencesStanford University
  • Marjolein Snippe
    • MRC Clinical Sciences CentreImperial College London
    • Blizard Institute of Cell and Molecular ScienceQueen Mary University of London
  • Izumi Sugihara
    • Department of Systems NeurophysiologyTokyo Medical and Dental University School of Medicine
    • Krasnow Institute for Advanced StudyGeorge Mason University

DOI: 10.1007/s12021-010-9095-5

Cite this article as:
Brown, K.M., Barrionuevo, G., Canty, A.J. et al. Neuroinform (2011) 9: 143. doi:10.1007/s12021-010-9095-5


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

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© Springer Science+Business Media, LLC 2011