A deep learning-based segmentation pipeline for profiling cellular morphodynamics using multiple types of live cell microscopy.

Journal: Cell reports methods
Published Date: Oct 27, 2021

Abstract

MOTIVATION: Quantitative studies of cellular morphodynamics rely on extracting leading-edge velocity time series based on accurate cell segmentation from live cell imaging. However, live cell imaging has numerous challenging issues regarding accurate edge localization. Fluorescence live cell imaging produces noisy and low-contrast images due to phototoxicity and photobleaching. While phase contrast microscopy is gentle to live cells, it suffers from the halo and shade-off artifacts that cannot be handled by conventional segmentation algorithms. Here, we present a deep learning-based pipeline, termed MARS-Net (Multiple-microscopy-type-based Accurate and Robust Segmentation Network), that utilizes transfer learning and data from multiple types of microscopy to localize cell edges with high accuracy, allowing quantitative profiling of cellular morphodynamics.

Authors

  • Junbong Jang
    Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, United States of America.
  • Chuangqi Wang
    Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, USA.
  • Xitong Zhang
    Michigan State University, Department of Computational Mathematics, Science & Engineering, East Lansing, Michigan, United States.
  • Hee June Choi
    Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, USA.
  • Xiang Pan
    Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, United States of America.
  • Bolun Lin
    Department of Computer Science, Worcester Polytechnic Institute, Worcester, MA 01609, USA.
  • Yudong Yu
    Robotics Engineering Program, Worcester Polytechnic Institute, Worcester, MA 01609, USA.
  • Carly Whittle
    Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USA.
  • Madison Ryan
    Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USA.
  • Yenyu Chen
    Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA 01609, USA.
  • Kwonmoo Lee
    Department of Biomedical Engineering, Worcester Polytechnic Institute, Worcester, MA, 01609, USA. klee@wpi.edu.

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