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Reconstruction and visualization of coordinated 3D cell migration based on optical flow

Animal development is marked by the repeated reorganization of cells and cell populations, which ultimately determine form and shape of the growing organism. One of the central questions in developmental biology is to understand precisely how cells reorganize, as well as how and to what extent this...

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Bibliographic Details
Main Authors: Kappe, Christopher P. (Author) , Schütz, Lucas (Author) , Hufnagel, Lars (Author) , Lemke, Steffen (Author) , Leitte, Heike (Author)
Format: Article (Journal)
Language:English
Published: 2016
In: IEEE transactions on visualization and computer graphics
Year: 2015, Volume: 22, Issue: 1, Pages: 995-1004
ISSN:1941-0506
DOI:10.1109/TVCG.2015.2467291
Online Access:Verlag, Volltext: http://dx.doi.org/10.1109/TVCG.2015.2467291
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Author Notes:Christopher P. Kappe, Student Member, IEEE, Lucas Schütz, Stefan Gunther, Lars Hufnagel, Steffen Lemke, and Heike Leitte, Member, IEEE

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245 1 0 |a Reconstruction and visualization of coordinated 3D cell migration based on optical flow  |c Christopher P. Kappe, Student Member, IEEE, Lucas Schütz, Stefan Gunther, Lars Hufnagel, Steffen Lemke, and Heike Leitte, Member, IEEE 
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520 |a Animal development is marked by the repeated reorganization of cells and cell populations, which ultimately determine form and shape of the growing organism. One of the central questions in developmental biology is to understand precisely how cells reorganize, as well as how and to what extent this reorganization is coordinated. While modern microscopes can record video data for every cell during animal development in 3D+t, analyzing these videos remains a major challenge: reconstruction of comprehensive cell tracks turned out to be very demanding especially with decreasing data quality and increasing cell densities. In this paper, we present an analysis pipeline for coordinated cellular motions in developing embryos based on the optical flow of a series of 3D images. We use numerical integration to reconstruct cellular long-term motions in the optical flow of the video, we take care of data validation, and we derive a LIC-based, dense flow visualization for the resulting pathlines. This approach allows us to handle low video quality such as noisy data or poorly separated cells, and it allows the biologists to get a comprehensive understanding of their data by capturing dynamic growth processes in stills. We validate our methods using three videos of growing fruit fly embryos. 
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