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

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 a...

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Detalles Bibliográficos
Autores principales: Hattab, Georges, Nattkemper, Tim W
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2019
Materias:
Applications Notes
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6513157/
https://www.ncbi.nlm.nih.gov/pubmed/30346487
http://dx.doi.org/10.1093/bioinformatics/bty889
Descripción
Sumario: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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