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Updated: Jun 6, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
Published on: February 15, 2017
Automated reconstruction of neuronal morphology: an overview
Duncan E Donohue1, Giorgio A Ascoli
1Center for Neural Informatics, Structure, & Plasticity and Molecular Neuroscience Department, Krasnow Institute for Advanced Study, George Mason University, 4400 University Drive, Fairfax, VA 22030-4422, USA. dedonohue@gmail.com
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Digital reconstruction of neuronal morphology is a powerful technique for investigating the nervous system. This process consists of tracing the axonal and dendritic arbors of neurons imaged by optical microscopy into a geometrical format suitable for quantitative analysis and computational modeling. Algorithmic automation of neuronal tracing promises to increase the speed, accuracy, and reproducibility of morphological reconstructions. Together with recent breakthroughs in cellular imaging and accelerating progress in optical microscopy, automated reconstruction of neuronal morphology will play a central role in the development of high throughput screening and the acquisition of connectomic data. Yet, despite continuous advances in image processing algorithms, to date manual tracing remains the overwhelming choice for digitizing neuronal morphology. We summarize the issues involved in automated reconstruction, overview the available techniques, and provide a realistic assessment of future perspectives.

