Journal article
Accurate Reconstruction of Cell and Particle Tracks from 3D Live Imaging Data
J Liepe, A Sim, H Weavers, L Ward, P Martin, MPH Stumpf
Cell Systems | CELL PRESS | Published : 2016
Abstract
Spatial structures often constrain the 3D movement of cells or particles in vivo, yet this information is obscured when microscopy data are analyzed using standard approaches. Here, we present methods, called unwrapping and Riemannian manifold learning, for mapping particle-tracking data along unseen and irregularly curved surfaces onto appropriate 2D representations. This is conceptually similar to the problem of reconstructing accurate geography from conventional Mercator maps, but our methods do not require prior knowledge of the environments’ physical structure. Unwrapping and Riemannian manifold learning accurately recover the underlying 2D geometry from 3D imaging data without the need..
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Awarded by Leverhulme Trust
Funding Acknowledgements
The project was in part granted by National Centre for the Replacement Refinement and Reduction of Animals in Research (NC3Rs) through a David Sainsbury Fellowship to J.L., by the BBSRC, The Leverhulme Trust and the Royal Society through a Wolfson Research Merit Award to M.P.H.S. A.S. is supported by the Human Frontiers Science Program project grant RGP0043/2013. L.W. and P.M. are supported by BBSRC and Cancer Research UK programme grants. P.M. is furthermore supported by a Wellcome Trust Senior Investigator Award. H.W. is funded by M.R.C. We acknowledge technical support from the Wolfson Bioimaging facility at the University of Bristol.