SeeVis—3D space-time cube rendering for visualization of microfluidics image data

MOTIVATION: Live cell imaging plays a pivotal role in understanding cell growth. Yet, there is a lack of visualization alternatives for quick qualitative characterization of colonies. RESULTS: SeeVis is a Python workflow for automated and qualitative visualization of time-lapse microscopy data. It automatically pre-processes the movie frames, finds particles, traces their trajectories and visualizes them in a space-time cube offering three different color mappings to highlight different features. It supports the user in developing a mental model for the data. SeeVis completes these steps in 1.15 s/frame and creates a visualization with a selected color mapping. AVAILABILITY AND IMPLEMENTATION: https://github.com/ghattab/seevis/ SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.