RT Journal Article
SR Electronic
T1 Content-aware frame interpolation (CAFI): Deep Learning-based temporal super-resolution for fast bioimaging
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 2021.11.02.466664
DO 10.1101/2021.11.02.466664
A1 Priessner, Martin
A1 Gaboriau, David C.A.
A1 Sheridan, Arlo
A1 Lenn, Tchern
A1 Chubb, Jonathan R.
A1 Manor, Uri
A1 Vilar, Ramon
A1 Laine, Romain F.
YR 2021
UL http://biorxiv.org/content/early/2021/11/03/2021.11.02.466664.abstract
AB 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.Competing Interest StatementThe authors have declared no competing interest.