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Abstract
The ability to create accurate geometric models of neuronal morphology is important for understanding the role of shape in information processing. Despite a significant amount of research on automating neuron reconstructions from image stacks obtained via microscopy, in practice most data are still collected manually. This paper describes Neuromantic, an open source system for three dimensional digital tracing of neurites. Neuromantic reconstructions are comparable in quality to those of existing commercial and freeware systems while balancing speed and accuracy of manual reconstruction. The combination of semi-automatic tracing, intuitive editing, and ability of visualizing large image stacks on standard computing platforms provides a versatile tool that can help address the reconstructions availability bottleneck. Practical considerations for reducing the computational time and space requirements of the extended algorithm are also discussed.
Highlights
Dendritic and axonal morphology plays an important role in determining neuronal behavior in health and disease (Kaufmann and Moser, 2000; Nasuto et al, 2001; Whalley et al, 2005)
Neuromorphological reconstruction using image processing is an important aspect of computational neuroanatomy
Available methods for neuronal reconstruction vary in their degree of automation (Meijering, 2010; Donohue and Ascoli, 2011; Svoboda, 2011)
Summary
Dendritic and axonal morphology plays an important role in determining neuronal behavior in health (van Elburg and van Ooyen, 2010) and disease (Kaufmann and Moser, 2000; Nasuto et al, 2001; Whalley et al, 2005). Neuromorphological reconstruction using image processing is an important aspect of computational neuroanatomy. Prisms are employed to visually overlay the microscope image onto a piece of paper, and the neuron is traced by hand. Primarily used for 2D tracings, 3D reconstructions can be derived from these with time consuming post-processing (Ropireddy et al, 2011). Digital segments are added by hand through a software interface, typically sequentially, beginning at the soma, and working down the dendritic tree. Semi-automatic [e.g., NeuronJ (Meijering et al, 2004; 2D reconstruction only) and Imaris (3D reconstruction)]. User interaction defines the basic morphology, such as identifying the tree root and terminations, but branch paths are traced by the computer. Automatic (e.g., Imaris, NeuronStudio; Rodriguez et al, 2003, AutoNeuron add-on for Neurolucida).
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