Abstract

This work aims to illustrate the impact of high-performance computing on bioimaging and bioimage analysis. The focus is on a data rich technique, lightsheet imaging (also called Selective Plane Illumination Microscopy or SPIM . Due to its advantages such as fast imaging of large volumes and low phototoxicity, the role of the technique e.g. in developmental biology or fast calcium indicator imaging cannot be overstated. However, the blocking factor for light sheet microscopy to reach its full potential is a computational one: typical datasets consist of time sequences of multi-tile, multi-angle, multi-colour 3D data stacks totalling terabytes of data that need complex processing. The processing steps we focus on can be categorized in pre-processing steps (denoising, deskewing, destriping, registration, stitching, deconvolution) and downstream analysis. Subsequent image analysis tasks are typically segmentation and tracking, followed by aggregating results across experiments (e.g. atlas creation). We discuss a deconvolution algorithm based on a new space varying image formation model, that would have been computationally prohibitive even on high-end workstations.