, Volume 9, Issue 2, pp 143-157

First online:

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

  • Kerry M. BrownAffiliated withKrasnow Institute for Advanced Study, George Mason University
  • , Germán BarrionuevoAffiliated withDepartment of Neuroscience, University of Pittsburgh
  • , Alison J. CantyAffiliated withMRC Clinical Sciences Centre, Imperial College LondonSchool of Medicine, University of Tasmania
  • , Vincenzo De PaolaAffiliated withMRC Clinical Sciences Centre, Imperial College London
  • , Judith A. HirschAffiliated withDepartment of Biological Sciences, University of Southern California
  • , Gregory S. X. E. JefferisAffiliated withDivision of Neurobiology, MRC Laboratory of Molecular Biology
  • , Ju LuAffiliated withDepartment of Biological Sciences, James H. Clark Center for Biomedical Engineering and Sciences, Stanford University
  • , Marjolein SnippeAffiliated withMRC Clinical Sciences Centre, Imperial College LondonBlizard Institute of Cell and Molecular Science, Queen Mary University of London
  • , Izumi SugiharaAffiliated withDepartment of Systems Neurophysiology, Tokyo Medical and Dental University School of Medicine

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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 (http://​diademchallenge.​org) to train, test, and aid development of automated reconstruction algorithms.


Axons Dendrites Neuroanatomy Tracing High-throughput Morphometry Optical imaging