Machine learning for faster and smarter fluorescence lifetime imaging microscopy
This work tackles the problem of inefficient FLIM analysis for biomedical researchers, but it is a topical review rather than a novel research contribution, making it incremental in nature.
The paper addresses the slow and computationally expensive process of fluorescence lifetime imaging microscopy (FLIM) by applying machine learning techniques to extract and interpret measurements from multi-dimensional FLIM datasets, resulting in substantial speed improvements and higher accuracy in classifying and segmenting images compared to conventional methods.
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.