Fully unsupervised symmetry-based mitosis detection in time-lapse cell microscopy

Topaz Gilad, Jose Reyes, Jia Yun Chen, Galit Lahav, Tammy Riklin Raviv

Research output: Contribution to journalArticlepeer-review

6 Scopus citations

Abstract

Motivation: Cell microscopy datasets have great diversity due to variability in cell types, imaging techniques and protocols. Existing methods are either tailored to specific datasets or are based on supervised learning, which requires comprehensive manual annotations. Using the latter approach, however, poses a significant difficulty due to the imbalance between the number of mitotic cells with respect to the entire cell population in a time-lapse microscopy sequence. Results: We present a fully unsupervised framework for both mitosis detection and mother-daughters association in fluorescence microscopy data. The proposed method accommodates the difficulty of the different cell appearances and dynamics. Addressing symmetric cell divisions, a key concept is utilizing daughters' similarity. Association is accomplished by defining cell neighborhood via a stochastic version of the Delaunay triangulation and optimization by dynamic programing. Our framework presents promising detection results for a variety of fluorescence microscopy datasets of different sources, including 2D and 3D sequences from the Cell Tracking Challenge. Availability and implementation: Code is available in github (github.com/topazgl/mitodix). Supplementary information: Supplementary data are available at Bioinformatics online.

Original languageEnglish
Pages (from-to)2644-2653
Number of pages10
JournalBioinformatics
Volume35
Issue number15
DOIs
StatePublished - 1 Aug 2019

ASJC Scopus subject areas

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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