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



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.


Attribute NameValues
  • Pierre C. Dagher

  • Evan L. Nichols

  • Cody J. Smith

  • Kenneth W. Dunn

  • Scott S. Howard

Journal or Work Title
  • Optics Letters

  • 44

  • 16

First Page
  • 3928

Last Page
  • 3931

  • 0146-9592

Publication Date
  • 2019-08

  • Niko A1R

  • OSA Publishing

Date Created
  • 2019-10-15

  • English

Departments and Units
Record Visibility Public
Content License
  • All rights reserved

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