Machine learning for faster and smarter fluorescence lifetime imaging microscopy

Abstract

Fluorescence lifetime imaging microscopy (FLIM) is a powerful technique in biomedical research that uses the fluorophore decay rate to provide additional contrast in fluorescence microscopy. However, at present, the calculation, analysis, and interpretation of FLIM is a complex, slow, and computationally expensive process. Machine learning (ML) techniques are well suited to extract and interpret measurements from multi-dimensional FLIM data sets with substantial improvement in speed over conventional methods. In this topical review, we first discuss the basics of FILM and ML. Second, we provide a summary of lifetime extraction strategies using ML and its applications in classifying and segmenting FILM images with higher accuracy compared to conventional methods. Finally, we discuss two potential directions to improve FLIM with ML with proof of concept demonstrations.

References in corpus (3)

• Deep Learning Microscopy
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• Instant Image Denoising Plugin for ImageJ using Convolutional Neural Networks

Cited by in corpus (5)

• Deep learning in biomedical optics
• Coupling a Recurrent Neural Network to SPAD TCSPC Systems for Real-time Fluorescence Lifetime Imaging
• Deep learning-based super-resolution fluorescence microscopy on small datasets
• Utilizing machine learning to improve the precision of fluorescence imaging of cavity-generated spin squeezed states
• Zero-Shot Denoising for Fluorescence Lifetime Imaging Microscopy with Intensity-Guided Learning