Neuromantic - from semi manual to semi automatic reconstruction of neuron morphology

Abstract

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The ability to create accurate geometric models of neuronal morphologyis important for understanding the role of shape in informationprocessing. Despite a significant amount of research on automating neuronreconstructions from image stacks obtained via microscopy, in practice mostdata are still collected manually.This paper describes Neuromantic, an open source system for threedimensional digital tracing of neurites. Neuromantic reconstructions arecomparable in quality to those of existing commercial and freeware systemswhile balancing speed and accuracy of manual reconstruction. Thecombination of semi-automatic tracing, intuitive editing, and ability ofvisualising large image stacks on standard computing platforms providesa versatile tool that can help address the reconstructions availabilitybottleneck. Practical considerations for reducing the computational time andspace requirements of the extended algorithm are also discussed.

Keywords

• morphology
• computational neuroscience
• LiveWire
• Neuromantic
• neuronal reconstruction