A Deep Learning-Based Workflow for High-Throughput and High-Quality Widefield Fluorescent Imaging of 3D Samples

3D widefield fluorescent microscopic (wFLM) imaging is a widespread technology used to study living three-dimensional samples, such as tumor spheroids during drug development. However, 3D wFLM imaging suffers from a severe trade-off between image quality and throughput limiting its applicability. In this project, we present a novel workflow that enables high-throughput 3D wFLM imaging, which has previously been impossible, and apply it to fluorescent indicators of cell health within 3D tumor spheroids. The workflow combines deep learning with state-of-the-art live-cell imaging techniques to speed up the acquisition of a fluorescent image of a three-dimensional sample by a factor of a hundred.