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. 2019 May 15;35(10):1802-1804.
doi: 10.1093/bioinformatics/bty889.

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

Georges Hattab  1   2 , Tim W Nattkemper  2
Affiliations

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

Georges Hattab et al. Bioinformatics. .

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.

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Figures

Fig. 1.
Fig. 1.
Color mappings demonstrated for dataset D1 (Schlüter et al., 2015). (a) RGB image (exposure: +60%). (b, c) The space-time cube is displayed with azimuth = 0∘ and elevation = 90° for two different color mappings and a 30% greyscale background: (b) TM, (c) PM, respectively. (d) PM shown onto a grid
See this image and copyright information in PMC

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