NITRC News Group Forum: the-diadem-metric--comparing-multiple-reconstructions-of-the-same-neuron

NITRC News Group Forum: the-diadem-metric--comparing-multiple-reconstructions-of-the-same-neuron http://stage.nitrcce.org/forum/forum.php?forum_id=2954 <div class="Abstract" lang="en"><a name="Abs1"></a>Abstract  <div class="normal">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 (<a href="http://diademchallenge.org">http://diademchallenge.org</a>) as a flexible instrument to measure morphological error or change in high-throughput reconstruction projects. </div> </div><ul> <li>Content Type Journal Article</li><li>Category Original Article</li><li>Pages 233-245</li><li>DOI 10.1007/s12021-011-9117-y</li><li>Authors<ul> <li>Todd A. Gillette, Center for Neural Informatics, Structures, & Plasticity, and Molecular Neuroscience Department, Krasnow Institute for Advanced Study, MS2A1 George Mason University, Fairfax, VA 22030, USA</li><li>Kerry M. Brown, Center for Neural Informatics, Structures, & Plasticity, and Molecular Neuroscience Department, Krasnow Institute for Advanced Study, MS2A1 George Mason University, Fairfax, VA 22030, USA</li><li>Giorgio A. Ascoli, Center for Neural Informatics, Structures, & Plasticity, and Molecular Neuroscience Department, Krasnow Institute for Advanced Study, MS2A1 George Mason University, Fairfax, VA 22030, USA</li> </ul></li> </ul><ul class="parents"> <ul class="details"> <li>Journal <a href="http://www.springerlink.com/content/120559/">Neuroinformatics</a></li><li>Online ISSN 1559-0089</li><li>Print ISSN 1539-2791</li> </ul><ul class="details"> <li>Journal Volume Volume 9</li> </ul><ul class="details"> <li>Journal Issue <a href="http://www.springerlink.com/content/q1w234205257/">Volume 9, Numbers 2-3</a></li> </ul> </ul> en-us Copyright 2000-2026 NITRC OSI Sat, 18 Apr 2026 7:08:53 GMT http://blogs.law.harvard.edu/tech/rss NITRC RSS generator