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2D + Time Object Tracking Using Fiji and ilastik

  • Andrea Urru
  • Miguel Angel González Ballester
  • Chong ZhangEmail author
Protocol
Part of the Methods in Molecular Biology book series (MIMB, volume 2040)

Abstract

Tracking cells is one of the main challenges in biology, as it often requires time-consuming annotations and the images can have a low signal-to-noise ratio while containing a large number of cells. Here we present two methods for detecting and tracking cells using the open-source Fiji and ilastik frameworks. A straightforward approach is described using Fiji, consisting of a pre-processing and segmentation phase followed by a tracking phase, based on the overlapping of objects along the image sequence. Using ilastik, a classifier is trained through manual annotations to both detect cells over the background and be able to recognize false detections and merging cells. We describe these two methods in a step-by-step fashion, using as example a time-lapse microscopy movie of HeLa cells.

Key words

Cell tracking Segmentation Classification Fiji ilastik 

Notes

Acknowledgments

This work was financed by a fellowship (LCF/BQ/IN17/11620069) from “la Caixa” Foundation (ID 100010434) and by the Spanish Ministry of Economy and Competitiveness grant MDM-1025-0502 through the Maria de Maeztu Units of Excellence in R&D program. We also acknowledge support from the European Commission through the NEUBIAS network (COST action no. CA15124).

Supplementary material

454918_1_En_20_MOESM1_ESM.zip (896 kb)
Electronic Supplementary File 1 (ZIP 897 kb)

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Copyright information

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

Authors and Affiliations

  • Andrea Urru
    • 1
  • Miguel Angel González Ballester
    • 1
    • 2
  • Chong Zhang
    • 1
    Email author
  1. 1.BCN-MedTech, DTICUniversitat Pompeu FabraBarcelonaSpain
  2. 2.ICREABarcelonaSpain

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