Deep learning-enhanced light-field imaging with continuous validation

Abstract

Light-field microscopy (LFM) has emerged as a powerful tool for fast volumetric image acquisition in biology, but its effective throughput and widespread use has been hampered by a computationally demanding and artefact-prone image reconstruction process. Here, we present a novel framework consisting of a hybrid light-field light-sheet microscope and deep learning-based volume reconstruction, where single light-sheet acquisitions continuously serve as training data and validation for the convolutional neural network reconstructing the LFM volume. Our network delivers high-quality reconstructions at video-rate throughput and we demonstrate the capabilities of our approach by imaging medaka heart dynamics and zebrafish neural activity.