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Focused Proofreading to Reconstruct Neural Connectomes from EM Images at Scale

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Deep Learning and Data Labeling for Medical Applications (DLMIA 2016, LABELS 2016)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 10008))

Abstract

Identifying complex neural circuitry from electron microscopic (EM) images may help unlock the mysteries of the brain. However, identifying this circuitry requires time-consuming, manual tracing (proofreading) due to the size and intricacy of these image datasets, thus limiting analysis to small brain regions. Potential avenues to improve scalability include automatic image segmentation and crowdsourcing, but current efforts have had limited success. In this paper, we propose a new strategy, focused proofreading, that works with automatic segmentation and aims to limit proofreading to areas that are most impactful to the resulting circuit. We then introduce a novel workflow, which exploits biological information such as synapses, and apply it to a large fly optic lobe dataset. Our techniques achieve significant tracing speedups without sacrificing quality. Furthermore, our methodology makes proofreading more accessible and could enhance the effectiveness of crowdsourcing.

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Acknowledgments

We thank Zhiyuan Lu for sample preparation; Shan Xu and Harald Hess for imaging; Pat Rivlin, Shin-ya Takemura, and the FlyEM proofreading team (Roxanne Aniceto, Lei-Ann Chang, Shirley Lauchie, Mathew Saunders, Christopher Sigmund, Satoko Takemura, Julie Tran) for reconstruction efforts; Donald Olbris for Raveler development; Toufiq Parag for generating segmentation classifiers; Louis Scheffer for useful discussions and suggestions.

Author information

Authors and Affiliations

  1. Janelia Research Campus, HHMI, Ashburn, Virginia, USA

    Stephen M. Plaza

Authors
  1. Stephen M. Plaza
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Corresponding author

Correspondence to Stephen M. Plaza .

Editor information

Editors and Affiliations

  1. University of Adelaide, Adelaide, South Australia, Australia

    Gustavo Carneiro

  2. Technical University of Munich, Garching, Germany

    Diana Mateus

  3. Technical University of Munich, Garching, Germany

    Loïc Peter

  4. University of Queensland, St Lucia, Queensland, Australia

    Andrew Bradley

  5. Universidade do Porto, Porto, Portugal

    João Manuel R. S. Tavares

  6. University of Oxford, Oxford, United Kingdom

    Vasileios Belagiannis

  7. Universidade Estadual Paulista, Bauru, Brazil

    João Paulo Papa

  8. Instituto Superior Técnico, Lisbon, Portugal

    Jacinto C. Nascimento

  9. Delft University of Technology, Delft, The Netherlands

    Marco Loog

  10. University of South Australia, Adelaide, South Australia, Australia

    Zhi Lu

  11. Universidade do Porto, Porto, Portugal

    Jaime S. Cardoso

  12. Google DeepMind, London, United Kingdom

    Julien Cornebise

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Plaza, S.M. (2016). Focused Proofreading to Reconstruct Neural Connectomes from EM Images at Scale. In: Carneiro, G., et al. Deep Learning and Data Labeling for Medical Applications. DLMIA LABELS 2016 2016. Lecture Notes in Computer Science(), vol 10008. Springer, Cham. https://doi.org/10.1007/978-3-319-46976-8_26

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  • DOI: https://doi.org/10.1007/978-3-319-46976-8_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-46975-1

  • Online ISBN: 978-3-319-46976-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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