Automatic segmentation of intravital fluorescence microscopy images by K-means clustering of FLIM phasors

Yide Zhang, Takashi Hato, Pierre C. Dagher, Evan L. Nichols, Cody J. Smith, Kenneth W. Dunn, Scott S. Howard

Research output: Contribution to journalArticle


Fluorescence lifetime imaging microscopy (FLIM) provides additional contrast for fluorophores with overlapping emission spectra. The phasor approach to FLIM greatly reduces the complexity of FLIM analysis and enables a useful image segmentation technique by selecting adjacent phasor points and labeling their corresponding pixels with different colors. This phasor labeling process, however, is empirical and could lead to biased results. In this Letter, we present a novel and unbiased approach to automate the phasor labeling process using an unsupervised machine learning technique, i.e., K-means clustering. In addition, we provide an open-source, user-friendly program that enables users to easily employ the proposed approach. We demonstrate successful image segmentation on 2D and 3D FLIM images of fixed cells and living animals acquired with two different FLIM systems. Finally, we evaluate how different parameters affect the segmentation result and provide a guideline for users to achieve optimal performance.

Original languageEnglish (US)
Pages (from-to)3928-3931
Number of pages4
JournalOptics Letters
Issue number16
StatePublished - Aug 15 2019


ASJC Scopus subject areas

  • Atomic and Molecular Physics, and Optics

Cite this