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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.