Cell Lineage Tracing in Lens-Free Microscopy Videos

  • Markus Rempfler
  • Sanjeev Kumar
  • Valentin Stierle
  • Philipp Paulitschke
  • Bjoern Andres
  • Bjoern H. Menze
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10434)

Abstract

In vitro experiments with cell cultures are essential for studying growth and migration behaviour and thus, for gaining a better understanding of cancer progression and its treatment. While recent progress in lens-free microscopy (LFM) has rendered it an inexpensive tool for continuous monitoring of these experiments, there is only little work on analysing such time-lapse sequences.

We propose (1) a cell detector for LFM images based on residual learning, and (2) a probabilistic model based on moral lineage tracing that explicitly handles multiple detections and temporal successor hypotheses by clustering and tracking simultaneously. (3) We benchmark our method on several hours of LFM time-lapse sequences in terms of detection and tracking scores. Finally, (4) we demonstrate its effectiveness for quantifying cell population dynamics.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Markus Rempfler
    • 1
    • 2
  • Sanjeev Kumar
    • 2
  • Valentin Stierle
    • 3
  • Philipp Paulitschke
    • 3
  • Bjoern Andres
    • 4
  • Bjoern H. Menze
    • 1
    • 2
  1. 1.Institute for Advanced StudyTechnical University of MunichMunichGermany
  2. 2.Department of InformaticsTechnical University of MunichMunichGermany
  3. 3.Faculty of PhysicsLudwig-Maximilians University of MunichMunichGermany
  4. 4.Max Planck Institute for InformaticsSaarbrückenGermany

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