Automatic segmentation of intravital fluorescence microscopy images by K-means clustering of FLIM phasors
Abstract
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.
Yide Zhang
Incoming Assistant Professor of ECEE and BME
My long-term research goal is to pioneer optical imaging technologies that surpass current limits in speed, accuracy, and accessibility, advancing translational research. With a foundation in electrical engineering, particularly in biomedical imaging and optics, my PhD work at the University of Notre Dame focused on advancing multiphoton fluorescence lifetime imaging microscopy and super-resolution microscopy, significantly reducing image generation time and cost. I developed an analog signal processing method that enables real-time streaming of fluorescence intensity and lifetime data, and created the first Poisson-Gaussian denoising dataset to benchmark image denoising algorithms for high-quality, real-time applications in biomedical research. As a postdoc at the California Institute of Technology (Caltech), my research expanded to include pioneering photoacoustic imaging techniques, enabling noninvasive and rapid imaging of hemodynamics in humans. In the realm of quantum imaging, I developed innovative techniques utilizing spatial and polarization entangled photon pairs, overcoming challenges such as poor signal-to-noise ratios and low resolvable pixel counts. Additionally, I advanced ultrafast imaging methods for visualizing passive current flows in myelinated axons and electromagnetic pulses in dielectrics. My research is currently funded by the National Institutes of Health (NIH) K99/R00 Pathway to Independence Award. I will join the University of Colorado Boulder (CU Boulder) as an Assistant Professor of Electrical, Computer & Energy Engineering (ECEE) and Biomedical Engineering (BME) in May 2025.