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

ABSTRACT:

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

Supplemen

SUBMITTER: Hattab G 

PROVIDER: S-EPMC6513157 | biostudies-literature | 2019 May

REPOSITORIES: biostudies-literature