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

Nattkemper TW, Hattab G (2019)
Bioinformatics 35(10): 1802-1804.

Zeitschriftenaufsatz | Veröffentlicht | Englisch
 
Abstract / Bemerkung
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 preprocesses 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.
Stichworte
Bioimage Informatics; Microfluidics
Erscheinungsjahr
2019
Zeitschriftentitel
Bioinformatics
Band
35
Ausgabe
10
Seite(n)
1802-1804
ISSN
0266-7061
Page URI
https://pub.uni-bielefeld.de/record/2931637

Zitieren

Nattkemper TW, Hattab G. SeeVis — 3D space-time cube rendering for visualization of microfluidics image data. Bioinformatics. 2019;35(10):1802-1804.
Nattkemper, T. W., & Hattab, G. (2019). SeeVis — 3D space-time cube rendering for visualization of microfluidics image data. Bioinformatics, 35(10), 1802-1804. doi:10.1093/bioinformatics/bty889
Nattkemper, T. W., and Hattab, G. (2019). SeeVis — 3D space-time cube rendering for visualization of microfluidics image data. Bioinformatics 35, 1802-1804.
Nattkemper, T.W., & Hattab, G., 2019. SeeVis — 3D space-time cube rendering for visualization of microfluidics image data. Bioinformatics, 35(10), p 1802-1804.
T.W. Nattkemper and G. Hattab, “SeeVis — 3D space-time cube rendering for visualization of microfluidics image data”, Bioinformatics, vol. 35, 2019, pp. 1802-1804.
Nattkemper, T.W., Hattab, G.: SeeVis — 3D space-time cube rendering for visualization of microfluidics image data. Bioinformatics. 35, 1802-1804 (2019).
Nattkemper, Tim Wilhelm, and Hattab, Georges. “SeeVis — 3D space-time cube rendering for visualization of microfluidics image data”. Bioinformatics 35.10 (2019): 1802-1804.
Alle Dateien verfügbar unter der/den folgenden Lizenz(en):
Copyright Statement:
This Item is protected by copyright and/or related rights. [...]
Link(s) zu Volltext(en)
Access Level
OA Open Access

14 References

Daten bereitgestellt von Europe PubMed Central.

CellProfiler Tracer: exploring and validating high-throughput, time-lapse microscopy image data
Bray M.A., Carpenter A.E.., 2015
Methods of digital video microscopy for colloidal studies
Crocker J., Grier D.., 1996
ViCAR: An Adaptive and Landmark-Free Registration of Time Lapse Image Data from Microfluidics Experiments.
Hattab G, Schluter JP, Becker A, Nattkemper TW., Front Genet 8(), 2017
PMID: 28620411
A Novel Methodology for Characterizing Cell Subpopulations in Automated Time-lapse Microscopy.
Hattab G, Wiesmann V, Becker A, Munzner T, Nattkemper TW., Front Bioeng Biotechnol 6(), 2018
PMID: 29541635
TLM-Tracker: software for cell segmentation, tracking and lineage analysis in time-lapse microscopy movies.
Klein J, Leupold S, Biegler I, Biedendieck R, Munch R, Jahn D., Bioinformatics 28(17), 2012
PMID: 22772947
Cell population tracking and lineage construction with spatiotemporal context.
Li K, Miller ED, Chen M, Kanade T, Weiss LE, Campbell PG., Med Image Anal 12(5), 2008
PMID: 18656418

McIntosh M., Bettenworth V.., 2017

Munzner T.., 2014
A survey of visualization for live cell imaging
Pretorius A.J.., 2017

Schlüter J.-P.., 2015
A linguistic approach to categorical color assignment for data visualization
Setlur V., Stone M.., 2016
Segmentation and tracking individual pseudomonas aeruginosa bacteria in dense populations of motile cells
Vallotton P.., 2009

Van S., Smith N.., 2015

Wiesmann V.., 2017

Export

Markieren/ Markierung löschen
Markierte Publikationen

Open Data PUB

Web of Science

Dieser Datensatz im Web of Science®

Quellen

PMID: 30346487
PubMed | Europe PMC

Suchen in

Google Scholar