PT - JOURNAL ARTICLE AU - David Minnen AU - Michał Januszewski AU - Alexander Shapson-Coe AU - Richard L. Schalek AU - Johannes Ballé AU - Jeff W. Lichtman AU - Viren Jain TI - Denoising-based Image Compression for Connectomics AID - 10.1101/2021.05.29.445828 DP - 2021 Jan 01 TA - bioRxiv PG - 2021.05.29.445828 4099 - http://biorxiv.org/content/early/2021/05/30/2021.05.29.445828.short 4100 - http://biorxiv.org/content/early/2021/05/30/2021.05.29.445828.full AB - 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 StatementThe authors have declared no competing interest.