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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

Supplementary data are available at Bioinformatics online.

SUBMITTER: Hattab G

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

REPOSITORIES: biostudies-literature

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

Hattab Georges G Nattkemper Tim W TW

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]

PMID: 30346487

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