Machine learning for faster and smarter fluorescence lifetime imaging microscopy

Article

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.

Attributes

Attribute NameValues
Creator
  • Varun Mannam

Contributor
  • Varun Mannam

  • Yide Zhang

  • Scott Howard

Journal or Work Title
  • Journal of Physics: Photonics

Volume
  • 2

Issue
  • 042005

First Page
  • 042005

Number of Pages
  • 18

Publication Date
  • 2020-09

Subject
  • Machine learning, FLIM, phasor, lifetime, image denoising, optics, photonics

Publisher
  • IOP publishing

Date Created
  • 2020-09-22

Language
  • English

Source
Departments and Units
Record Visibility Public
Content License

Digital Object Identifier

doi:10.1088/2515-7647/abac1a

This DOI is the best way to cite this article.

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