The DIADEM and Beyond

Among the many challenges in understanding the functions of healthy and diseased brain is our limited ability to thoroughly analyze its structure and activity at neuronal, circuit and network levels. Despite the advent of computer technology, neuronal reconstructions are still largely performed by hand. Reconstructing a single cell may take months, presenting a bottleneck to progress. To meet this challenge, the DIADEM (Digital Reconstruction of Axonal and Dendritic Morphology) Challenge provided incentives for the development of computer algorithms to support accurate high-speed, automated three-dimensional (3D) reconstruction of neuronal projections. Together with the sponsors of the DIADEM Challenge, several Institutes of the National Institutes of Health (NIH) co-sponsored the DIADEM Scientific Conference in conjunction with the final phase of the DIADEM Challenge. The conference brought together computational scientists and experimental neuroscientists to evaluate the advances made by the finalist teams, to summarize stateof-the-art technologies, and to discuss the remaining challenges and future research directions. Over sixty scientists attended the conference, including many postdoctoral fellows and graduate students. The conference opened with remarks by the sponsors, which was followed by an overview of “The Future of Connectomics” by keynote speaker Dr. Jeff Lichtman of Harvard University. The original articles, mini-reviews, and commentaries published in this special issue of Neuroinformatics address central themes of the DIADEM Challenge. This editorial provides a brief summary of the Challenge and the Conference. Additional DIADEM legacy material can be found at the DIADEM Challenge website coordinated and maintained by Dr. Giorgio Ascoli’s laboratory (http:// diademchallenge.org).

[1]  R. Douglas,et al.  Opening the grey box , 1991, Trends in Neurosciences.

[2]  Ju Lu,et al.  Semi-Automated Reconstruction of Neural Processes from Large Numbers of Fluorescence Images , 2009, PloS one.

[3]  Eugene W. Myers,et al.  Proof-editing is the Bottleneck Of 3D Neuron Reconstruction: The Problem and Solutions , 2011, Neuroinformatics.

[4]  Ju Lu,et al.  The DIADEM Data Sets: Representative Light Microscopy Images of Neuronal Morphology to Advance Automation of Digital Reconstructions , 2011, Neuroinformatics.

[5]  Kevan A. C. Martin,et al.  What’s Black and White About the Grey Matter? , 2011, Neuroinformatics.

[6]  Stephen L. Senft,et al.  A Brief History of Neuronal Reconstruction , 2011, Neuroinformatics.

[7]  James Kozloski,et al.  Automated Reconstruction of Neural Tissue and the Role of Large-Scale Simulation , 2011, Neuroinformatics.

[8]  Hollis T. Cline,et al.  Diadem X: Automated 4 Dimensional Analysis of Morphological Data , 2011, Neuroinformatics.

[9]  Alison J. Canty,et al.  Axonal Reconstructions Going Live , 2011, Neuroinformatics.

[10]  Pascal Fua,et al.  Automated Reconstruction of Dendritic and Axonal Trees by Global Optimization with Geometric Priors , 2011, Neuroinformatics.

[11]  Eugene W. Myers,et al.  Automated Reconstruction of Neuronal Morphology Based on Local Geometrical and Global Structural Models , 2011, Neuroinformatics.

[12]  Giorgio A. Ascoli,et al.  The DIADEM Metric: Comparing Multiple Reconstructions of the Same Neuron , 2011, Neuroinformatics.

[13]  Vivek Mehta,et al.  Automated Tracing of Neurites from Light Microscopy Stacks of Images , 2011, Neuroinformatics.

[14]  Deniz Erdogmus,et al.  Principal Curves as Skeletons of Tubular Objects , 2011, Neuroinformatics.

[15]  Ju Lu,et al.  Neuronal Tracing for Connectomic Studies , 2011, Neuroinformatics.