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bioRxiv · 10.1101/2021.05.29.445828

Denoising-based Image Compression for Connectomics

Minnen, D.·Januszewski, M.·Shapson-Coe, A.·Schalek, R. L.·Balle, J.·Lichtman, J. W.·Jain, V.
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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 3d reconstruction and synaptic detection accuracy.

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Minnen, D., Januszewski, M., Shapson-Coe, A., Schalek, R. L., Balle, J., Lichtman, J. W., Jain, V.. 2021-05-30. Denoising-based Image Compression for Connectomics. https://doi.org/10.1101/2021.05.29.445828

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