Abstract
Modern microscopy modalities create a data deluge with gigabytes of data generated each second, or terabytes per day. Storing and processing these data is a severe bottleneck. We argue that this is an artifact of the images being represented on pixels. To address the root of the problem, we here propose the Adaptive Particle Representation (APR) as an image-content-aware representation of fluorescence microscopy images. The APR replaces pixel images to overcome computational and memory bottlenecks in storage and processing pipelines for studying spatiotemporal processes in biology using fluorescence microscopy. We present the ideas, concepts, and algorithms and validate them using noisy 3D image data. We show how the APR adapts to the information content of an image without reducing image quality. We then show that the adaptivity of the APR provides orders of magnitude benefits across a range of image-processing tasks. Therefore, the APR provides a simple, extendable, and efficient content-aware representation of images that could be useful for many imaging modalities in order to relax current data and processing bottlenecks.
