# Machine learning for faster and smarter fluorescence lifetime imaging microscopy

arXiv: https://arxiv.org/abs/2008.02320 · HTML: https://mlsanity.com/paper/2008.02320/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Aug 2020.

## Built on by (most cited first, 5 of 5)

- [Coupling a Recurrent Neural Network to SPAD TCSPC Systems for Real-time Fluorescence Lifetime Imaging](https://mlsanity.com/paper/2306.15599/) (2023)
- [Deep learning-based super-resolution fluorescence microscopy on small datasets](https://mlsanity.com/paper/2103.04989/) (2021)
- [Single-sample image-fusion upsampling of fluorescence lifetime images](https://mlsanity.com/paper/2404.13102/) (2024)
- [Convolutional Neural Network Denoising in Fluorescence Lifetime Imaging Microscopy (FLIM)](https://mlsanity.com/paper/2103.05448/) (2021)
- [Low dosage 3D volume fluorescence microscopy imaging using compressive sensing](https://mlsanity.com/paper/2201.00820/) (2022)

## Abstract (arXiv)

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.