Abstract

Herein, we present a novel workflow based on Deep Learning (DL) trained on synthetic data to quantify fluorescence lifetime imaging of experimental data across multiple microscopic and macroscopic applications with unprecedented accuracy and computational speed.

© 2019 The Author(s)

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