Machine learning for faster and smarter fluorescence lifetime imaging microscopy

TL;DR

This paper reviews how machine learning can significantly accelerate and enhance fluorescence lifetime imaging microscopy (FLIM) analysis, enabling faster, more accurate biomedical imaging and interpretation.

Contribution

It provides a comprehensive overview of ML-based FLIM analysis methods, highlighting recent advances and proposing future directions for improving FLIM with ML techniques.

Findings

ML techniques improve FLIM analysis speed and accuracy

ML enables better classification and segmentation of FLIM images

Proof of concept demonstrates potential for future FLIM enhancements

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.