Dataset Information
SeeVis-3D space-time cube rendering for visualization of microfluidics image data.
Ontology highlight
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
Supplementary data are available at Bioinformatics online.
SUBMITTER: Hattab G
PROVIDER: S-EPMC6513157 | biostudies-literature | 2019 May
REPOSITORIES: biostudies-literature
Publications
SeeVis-3D space-time cube rendering for visualization of microfluidics image data.
Bioinformatics (Oxford, England) 20190501 10
<h4>Motivation</h4>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.<h4>Results</h4>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 diffe ...[more]