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Denoising-based Image Compression for Connectomics

View ORCID ProfileDavid Minnen, View ORCID ProfileMichał Januszewski, Alexander Shapson-Coe, Richard L. Schalek, Johannes Ballé, Jeff W. Lichtman, View ORCID ProfileViren Jain
doi: https://doi.org/10.1101/2021.05.29.445828
David Minnen
1Google Research
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Michał Januszewski
1Google Research
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Alexander Shapson-Coe
2Department of Molecular and Cellular Biology & Center for Brain Science, Harvard University
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Richard L. Schalek
2Department of Molecular and Cellular Biology & Center for Brain Science, Harvard University
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Johannes Ballé
1Google Research
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Jeff W. Lichtman
2Department of Molecular and Cellular Biology & Center for Brain Science, Harvard University
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Viren Jain
1Google Research
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  • For correspondence: viren@google.com
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Abstract

Connectomic reconstruction of neural circuits relies on nanometer resolution microscopy which produces on the order of a petabyte of imagery for each cubic millimeter of brain tissue. The cost of storing such data is a significant barrier to broadening the use of connectomic approaches and scaling to even larger volumes. We present an image compression approach that uses machine learning-based denoising and standard image codecs to compress raw electron microscopy imagery of neuropil up to 17-fold with negligible loss of reconstruction accuracy.

Competing Interest Statement

The authors have declared no competing interest.

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-NC-ND 4.0 International license.
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Posted May 30, 2021.
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Denoising-based Image Compression for Connectomics
David Minnen, Michał Januszewski, Alexander Shapson-Coe, Richard L. Schalek, Johannes Ballé, Jeff W. Lichtman, Viren Jain
bioRxiv 2021.05.29.445828; doi: https://doi.org/10.1101/2021.05.29.445828
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Denoising-based Image Compression for Connectomics
David Minnen, Michał Januszewski, Alexander Shapson-Coe, Richard L. Schalek, Johannes Ballé, Jeff W. Lichtman, Viren Jain
bioRxiv 2021.05.29.445828; doi: https://doi.org/10.1101/2021.05.29.445828

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