A Cell Segmentation/Tracking Tool Based on Machine Learning

  • Heather S. Deter
  • Marta Dies
  • Courtney C. Cameron
  • Nicholas C. ButzinEmail author
  • Javier BucetaEmail author
Part of the Methods in Molecular Biology book series (MIMB, volume 2040)


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.

Key words

Computational image analysis Single-cell quantification Cell lineage analysis Cell segmentation Cell tracking Machine learning Fluorescence microscopy Bacterial growth 

Supplementary material

454918_1_En_19_MOESM1_ESM.docx (26 kb)
Supplementary Table S1 Abbreviations (DOCX 26 kb)


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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Biology and Microbiology DepartmentSouth Dakota State UniversityBrookingsUSA
  2. 2.Chemical and Biomolecular Engineering DepartmentLehigh UniversityBethlehemUSA
  3. 3.Bioengineering DepartmentLehigh UniversityBethlehemUSA

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