A Bayesian Topological Framework for the Identification and Reconstruction of Subcellular Motion
Abstract
Microscopy imaging allows detailed observations of intracellular movements and the acquisition of large datasets that can be fully analyzed only by automated algorithms. Here, we develop a computational method for the automatic identification and reconstruction of trajectories followed by subcellular particles captured in microscopy image data. The method operates on stacks of raw image data and computes the complete set of contained trajectories. The method utilizes topological data analysis and standard image processing techniques and makes no assumptions about the underlying dynamics besides continuity. We test the developed method successfully against artificial and experimental datasets. Application of the method on the experimental data reveals good agreement with manual tracking and benchmarking yields performance scores competitive to the existing state-of-the-art tracking methods.
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Title of paper: A Bayesian Topological Framework for the Identification and Reconstruction of Subcellular Motion
Authors: Ioannis Sgouralis, Andreas Nebenfuehr, and Vasileios Maroulas
File: M109575_01.pdf
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Contents: The supplement contains: (i) a glossary summarizing the notation used throughout this study, (ii) a pseudocode implementation of the proposed method, (iii) example analyses of additional experimental datasets.
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© 2017, Society for Industrial and Applied Mathematics.
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Submitted: 26 September 2016
Accepted: 29 March 2017
Published online: 13 June 2017
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Air Force Office of Scientific Research https://doi.org/10.13039/100000181 : FA9550-15-1-0103
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National Institute for Mathematical and Biological Synthesis https://doi.org/10.13039/100008947 : DBI-1300426
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Simons Foundation https://doi.org/10.13039/100000893 : 279870
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