BIDS Data Science Lecture: Do as eye do: efficient content-adaptive processing and storage of large fluorescence images
Lecture | April 10 | 3:10-4 p.m. | 190 Doe Library
Bevan Cheeseman, Applied Mathematician, Max Planck Institute of Molecular Cell Biology
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 developed 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 not only overcomes storage bottlenecks, as data compression does, but additionally overcomes memory and processing bottlenecks since the adaptivity can be used during processing tasks. In this talk, I will introduce the ideas and concepts of the APR, its performance on experimental data, and show how the APR can be used to enhance, rather than replace, existing algorithms and approaches, including applications to machine learning. Beyond image-processing I will also present how the APR can be used for adaptive resolution simulations, and discuss work on robust methods for data-driven model discovery for spatial-temporal data.