Abstract

The reconstruction of neuron morphology allows to investigate how the brain works, which is one of the foremost challenges in neuroscience. This process aims at extracting the neuronal structures from microscopic imaging data. The great advances in microscopic technologies have made a huge amount of data available at the micro-, or even lower, resolution where manual inspection is time consuming, prone to error and utterly impractical. This has motivated the development of methods to automatically trace the neuronal structures, a task also known as neuron tracing. This paper surveys the latest neuron tracing methods available in the scientific literature as well as a selection of significant older papers to better place these proposals into context. They are categorized into global processing, local processing and meta-algorithm approaches. Furthermore, we point out the algorithmic components used to design each method and we report information on the datasets and the performance metrics used.