Nonparametric empirical Bayesian framework for fluorescence-lifetime imaging microscopy

Shulei Wang, Jenu V. Chacko, Abdul K. Sagar, Kevin W. Eliceiri, Ming Yuan

Research output: Contribution to journalArticlepeer-review


Fluorescence lifetime imaging microscopy (FLIM) is a powerful imaging tool used to study the molecular environment of flurophores. In time domain FLIM, extracting lifetime from fluorophores signals entails fitting data to a decaying exponential distribution function. However, most existing techniques for this purpose need large amounts of photons at each pixel and a long computation time, thus making it difficult to obtain reliable inference in applications requiring either short acquisition or minimal computation time. In this work, we introduce a new nonparametric empirical Bayesian framework for FLIM data analysis (NEB-FLIM), leading to both improved pixel-wise lifetime estimation and a more robust and computationally efficient integral property inference. This framework is developed based on a newly proposed hierarchical statistical model for FLIM data and adopts a novel nonparametric maximum likelihood estimator to estimate the prior distribution. To demonstrate the merit of the proposed framework, we applied it on both simulated and real biological datasets and compared it with previous classical methods on these datasets.

Original languageEnglish (US)
Pages (from-to)5497-5517
Number of pages21
JournalBiomedical Optics Express
Issue number11
StatePublished - Nov 1 2019
Externally publishedYes

ASJC Scopus subject areas

  • Biotechnology
  • Atomic and Molecular Physics, and Optics


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