SeeVis-3D space-time cube rendering for visualization of microfluidics image data.
Where this comes from
- Record sourced from PubMed, PMID 30346487.
- Also identified by DOI 10.1093/bioinformatics/bty889 and PMC identifier 6513157.
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Abstract
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. 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. https://github.com/ghattab/seevis/. Supplementary data are available at Bioinformatics online.
Medical subject headings
- Microfluidics
- Software