A Cell Segmentation/Tracking Tool Based on Machine Learning.

Journal: Methods in molecular biology (Clifton, N.J.)
Published Date: Jan 1, 2019

Abstract

The ability to gain quantifiable, single-cell data from time-lapse microscopy images is dependent upon cell segmentation and tracking. Here, we present a detailed protocol for obtaining quality time-lapse movies and introduce a method to identify (segment) and track cells based on machine learning techniques (Fiji's Trainable Weka Segmentation) and custom, open-source Python scripts. To provide a hands-on experience, we provide datasets obtained using the aforementioned protocol.

Authors

  • Heather S Deter
    Biology and Microbiology Department, South Dakota State University, Brookings, SD, USA.
  • Marta Dies
    Chemical and Biomolecular Engineering Department, Lehigh University, Bethlehem, PA, USA.
  • Courtney C Cameron
    Biology and Microbiology Department, South Dakota State University, Brookings, SD, USA.
  • Nicholas C Butzin
    Biology and Microbiology Department, South Dakota State University, Brookings, SD, USA. nicholas.butzin@sdstate.edu.
  • Javier Buceta
    Chemical and Biomolecular Engineering Department, Lehigh University, Bethlehem, PA, USA. jbuceta@lehigh.edu.

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