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- W3171924624 abstract "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." @default.
- W3171924624 created "2021-06-22" @default.
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- W3171924624 date "2021-05-30" @default.
- W3171924624 modified "2023-10-16" @default.
- W3171924624 title "Denoising-based Image Compression for Connectomics" @default.
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- W3171924624 doi "https://doi.org/10.1101/2021.05.29.445828" @default.
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