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Automated Neuron Tracing Methods: An Updated Account

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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.

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Notes

  1. The dataset used in Zhou et al. (2015b) is not reported in Table 2 since no information is available.

  2. http://www.davidmayerich.net/resources/software.shtml.

  3. We do not report further details on these two datasets since they are not neuronal images datasets.

  4. The interested readers may refer to equations 6 and 7 in (Al-Kofahi et al. 2002) for the formal presentation of this step.

  5. www.vaa3d.org.

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Acknowledgments

The Authors are grateful to the anonymous referees for their valuable comments which greatly helped to improve the first version of this paper.

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Authors and Affiliations

  1. Department of Engineering, University Campus Bio-Medico of Rome, Roma, Italy

    Ludovica Acciai, Paolo Soda & Giulio Iannello

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  1. Ludovica Acciai
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  3. Giulio Iannello
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Correspondence to Giulio Iannello.

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None declared. All the authors are supported by University Campus Bio-Medico of Rome.

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Acciai, L., Soda, P. & Iannello, G. Automated Neuron Tracing Methods: An Updated Account. Neuroinform 14, 353–367 (2016). https://doi.org/10.1007/s12021-016-9310-0

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