Imperial College London
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Prof. Ramon Vilar

Faculty of Natural Sciences, Department of Chemistry

Prof of Medicinal Inorganic Chemistry & Vice-Dean (Research)
 
 
 
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Contact

 

+44 (0)20 7594 1967r.vilar Website

 
 
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Location

 

301HMolecular Sciences Research HubWhite City Campus

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Summary

 

Publications

Citation

BibTex format

@article{Priessner:2021:10.1101/2021.11.02.466664,
author = {Priessner, M and Gaboriau, DCA and Sheridan, A and Lenn, T and Chubb, JR and Manor, U and Vilar, R and Laine, RF},
doi = {10.1101/2021.11.02.466664},
title = {Content-aware frame interpolation (CAFI): Deep Learning-based temporal super-resolution for fast bioimaging},
url = {http://dx.doi.org/10.1101/2021.11.02.466664},
year = {2021}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - <jats:p>The development of high-resolution microscopes has made it possible to investigate cellular processes in 4D (3D over time). However, observing fast cellular dynamics remains challenging as a consequence of photobleaching and phototoxicity. These issues become increasingly problematic with the depth of the volume acquired and the speed of the biological events of interest. Here, we report the implementation of two content-aware frame interpolation (CAFI) deep learning networks, Zooming SlowMo (ZS) and Depth-Aware Video Frame Interpolation (DAIN), based on combinations of recurrent neural networks, that are highly suited for accurately predicting images in between image pairs, therefore improving the temporal resolution of image series as a post-acquisition analysis step. We show that CAFI predictions are capable of understanding the motion context of biological structures to perform better than standard interpolation methods. We benchmark CAFI’s performance on six different datasets, obtained from three different microscopy modalities (point-scanning confocal, spinning-disk confocal and confocal brightfield microscopy). We demonstrate its capabilities for single-particle tracking methods applied to the study of lysosome trafficking. CAFI therefore allows for reduced light exposure and phototoxicity on the sample and extends the possibility of long-term live-cell imaging. Both DAIN and ZS as well as the training and testing data are made available for use by the wider community via the ZeroCostDL4Mic platform.</jats:p>
AU  - Priessner,M
AU  - Gaboriau,DCA
AU  - Sheridan,A
AU  - Lenn,T
AU  - Chubb,JR
AU  - Manor,U
AU  - Vilar,R
AU  - Laine,RF
DO  - 10.1101/2021.11.02.466664
PY  - 2021///
TI  - Content-aware frame interpolation (CAFI): Deep Learning-based temporal super-resolution for fast bioimaging
UR  - http://dx.doi.org/10.1101/2021.11.02.466664
ER  -
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