UNET-FLIM: A Deep Learning-Based Lifetime Determination Method Facilitating Real-Time Monitoring of Rapid Lysosomal pH Variations in Living Cells

Abstract

Lifetime determination plays a crucial role in fluorescence lifetime imaging microscopy (FLIM). We introduce UNET-FLIM, a deep learning architecture based on a one-dimensional U-net, specifically designed for lifetime determination. UNET-FLIM focuses on handling low photon count data with high background noise levels, which are commonly encountered in fast FLIM applications. The proposed network can be effectively trained using simulated decay curves, making it adaptable to various time-domain FLIM systems. Our evaluations of simulated data demonstrate that UNET-FLIM robustly estimates lifetimes and proportions, even when the signal photon count is extremely low and background noise levels are high. Remarkably, UNET-FLIM’s insensitivity to noise and minimal photon count requirements facilitate fast FLIM imaging and real-time lifetime analysis. We demonstrate its potential by applying it to monitor rapid lysosomal pH variations in living cells during in situ acetic acid treatment, all without necessitating any modifications to existing FLIM systems.