Adaptive particle representation of fluorescence microscopy images

Bevan L. Cheeseman, Ulrik Günther, Krzysztof Gonciarz, Mateusz Susik and Ivo F. Sbalzarini ()
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Bevan L. Cheeseman: Chair of Scientific Computing for Systems Biology, Faculty of Computer Science, TU Dresden
Ulrik Günther: Chair of Scientific Computing for Systems Biology, Faculty of Computer Science, TU Dresden
Krzysztof Gonciarz: Chair of Scientific Computing for Systems Biology, Faculty of Computer Science, TU Dresden
Mateusz Susik: Chair of Scientific Computing for Systems Biology, Faculty of Computer Science, TU Dresden
Ivo F. Sbalzarini: Chair of Scientific Computing for Systems Biology, Faculty of Computer Science, TU Dresden

Nature Communications, 2018, vol. 9, issue 1, 1-13

Abstract: Abstract Modern microscopes create a data deluge with gigabytes of data generated each second, and terabytes per day. Storing and processing this data is a severe bottleneck, not fully alleviated by data compression. We argue that this is because images are processed as grids of pixels. To address this, we propose a content-adaptive representation of fluorescence microscopy images, the Adaptive Particle Representation (APR). The APR replaces pixels with particles positioned according to image content. The APR overcomes storage bottlenecks, as data compression does, but additionally overcomes memory and processing bottlenecks. Using noisy 3D images, we show that the APR adaptively represents the content of an image while maintaining image quality and that it enables orders of magnitude benefits across a range of image processing tasks. The APR provides a simple and efficient content-aware representation of fluosrescence microscopy images.

Date: 2018
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DOI: 10.1038/s41467-018-07390-9

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