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


Brown, KM and Barrionuevo, G and Canty, AJ and De Paola, V and Hirsch, JA and Jefferis, GSXE and Lu, J and Snippe, M and Sugihara, I and Ascoli, GA, The DIADEM Data Sets: Representative Light Microscopy Images of Neuronal Morphology to Advance Automation of Digital Reconstructions, Neuroinformatics, 9, (2-3) pp. 143-157. ISSN 1539-2791 (2011) [Refereed Article]

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Copyright 2011 Springer Science+Business Media.

DOI: 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

Item Details

Item Type:Refereed Article
Research Division:Biomedical and Clinical Sciences
Research Group:Neurosciences
Research Field:Central nervous system
Objective Division:Expanding Knowledge
Objective Group:Expanding knowledge
Objective Field:Expanding knowledge in the information and computing sciences
UTAS Author:Canty, AJ (Associate Professor Alison Canty)
ID Code:69926
Year Published:2011
Web of Science® Times Cited:90
Deposited By:Medicine
Deposited On:2011-05-25
Last Modified:2017-11-06

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