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Citizen science, cells and CNNs – deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations

View ORCID ProfileHelen Spiers, Harry Songhurst, Luke Nightingale, View ORCID ProfileJoost de Folter, Roger Hutchings, Christopher J Peddie, View ORCID ProfileAnne Weston, Amy Strange, View ORCID ProfileSteve Hindmarsh, View ORCID ProfileChris Lintott, View ORCID ProfileLucy M Collinson, View ORCID ProfileMartin L Jones
doi: https://doi.org/10.1101/2020.07.28.223024
Helen Spiers
1Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, NW1 1AT, United Kingdom
2Department of Physics, University of Oxford, Oxford, OX1 3RH, United Kingdom
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Harry Songhurst
1Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, NW1 1AT, United Kingdom
3University of Manchester, Manchester, M13 9PL, United Kingdom
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Luke Nightingale
4Scientific Computing Science Technology Platform, The Francis Crick Institute, London, NW1 1AT, United Kingdom
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Joost de Folter
4Scientific Computing Science Technology Platform, The Francis Crick Institute, London, NW1 1AT, United Kingdom
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Roger Hutchings
2Department of Physics, University of Oxford, Oxford, OX1 3RH, United Kingdom
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Christopher J Peddie
1Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, NW1 1AT, United Kingdom
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Anne Weston
1Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, NW1 1AT, United Kingdom
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Amy Strange
4Scientific Computing Science Technology Platform, The Francis Crick Institute, London, NW1 1AT, United Kingdom
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Steve Hindmarsh
4Scientific Computing Science Technology Platform, The Francis Crick Institute, London, NW1 1AT, United Kingdom
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Chris Lintott
1Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, NW1 1AT, United Kingdom
2Department of Physics, University of Oxford, Oxford, OX1 3RH, United Kingdom
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Lucy M Collinson
1Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, NW1 1AT, United Kingdom
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Martin L Jones
1Electron Microscopy Science Technology Platform, The Francis Crick Institute, London, NW1 1AT, United Kingdom
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Abstract

Advancements in volume electron microscopy mean it is now possible to generate thousands of serial images at nanometre resolution overnight, yet the gold standard approach for data analysis remains manual segmentation by an expert microscopist, resulting in a critical research bottleneck. Although some machine learning approaches exist in this domain, we remain far from realising the aspiration of a highly accurate, yet generic, automated analysis approach, with a major obstacle being lack of sufficient high-quality ground-truth data. To address this, we developed a novel citizen science project, Etch a Cell, to enable volunteers to manually segment the nuclear envelope of HeLa cells imaged with Serial Blockface SEM. We present our approach for aggregating multiple volunteer annotations to generate a high quality consensus segmentation, and demonstrate that data produced exclusively by volunteers can be used to train a highly accurate machine learning algorithm for automatic segmentation of the nuclear envelope, which we share here, in addition to our archived benchmark data.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • * This publication has been made possible by the participation of volunteers in the Etch A Cell project. Their contributions are acknowledged at www.zooniverse.org/projects/h-spiers/etch-a-cell/about/results

  • https://www.ebi.ac.uk/pdbe/emdb/empiar/entry/10094/

  • https://www.zooniverse.org/projects/h-spiers/etch-a-cell

  • https://github.com/FrancisCrickInstitute/Etch-a-Cell-Nuclear-Envelope

  • https://www.ebi.ac.uk/biostudies/files/S-BSST448

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY 4.0 International license.
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Posted July 29, 2020.
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Citizen science, cells and CNNs – deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations
Helen Spiers, Harry Songhurst, Luke Nightingale, Joost de Folter, Roger Hutchings, Christopher J Peddie, Anne Weston, Amy Strange, Steve Hindmarsh, Chris Lintott, Lucy M Collinson, Martin L Jones
bioRxiv 2020.07.28.223024; doi: https://doi.org/10.1101/2020.07.28.223024
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Citizen science, cells and CNNs – deep learning for automatic segmentation of the nuclear envelope in electron microscopy data, trained with volunteer segmentations
Helen Spiers, Harry Songhurst, Luke Nightingale, Joost de Folter, Roger Hutchings, Christopher J Peddie, Anne Weston, Amy Strange, Steve Hindmarsh, Chris Lintott, Lucy M Collinson, Martin L Jones
bioRxiv 2020.07.28.223024; doi: https://doi.org/10.1101/2020.07.28.223024

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