Congratulations to Nils Norlin and his co-authors who recently published their work in Nature Methods!

Deep learning-enhanced light-field imaging with continuous validation

Nils Wagner, Fynn Beuttenmueller, Nils Norlin, Jakob Gierten, Juan Carlos Boffi, Joachim Wittbrodt, Martin Weigert, Lars Hufnagel, Robert Prevedel & Anna Kreshuk 
Nature Methods volume 18, pages557–563 (2021)

Abstract

Visualizing dynamic processes over large, three-dimensional fields of view at high speed is essential for many applications in the life sciences. Light-field microscopy (LFM) has emerged as a tool for fast volumetric image acquisition, but its effective throughput and widespread use in biology has been hampered by a computationally demanding and artifact-prone image reconstruction process. Here, we present a framework for artificial intelligence–enhanced microscopy, integrating a hybrid light-field light-sheet microscope and deep learning–based volume reconstruction. In our approach, concomitantly acquired, high-resolution two-dimensional light-sheet images continuously serve as training data and validation for the convolutional neural network reconstructing the raw LFM data during extended volumetric time-lapse imaging experiments. Our network delivers high-quality three-dimensional reconstructions at video-rate throughput, which can be further refined based on the high-resolution light-sheet images. We demonstrate the capabilities of our approach by imaging medaka heart dynamics and zebrafish neural activity with volumetric imaging rates up to 100 Hz. 
 

Article available at Nature Methods: https://www.nature.com/articles/s41592-021-01136-0