Label free cell-tracking and division detection based on 2D time-lapse images for lineage analysis of early embryo development

Marcelo Cicconet, Michelle Gutwein, Kristin C. Gunsalus, Davi Geiger

Research output: Contribution to journalArticle

Abstract

In this paper we report a database and a series of techniques related to the problem of tracking cells, and detecting their divisions, in time-lapse movies of mammalian embryos. Our contributions are (1) a method for counting embryos in a well, and cropping each individual embryo across frames, to create individual movies for cell tracking; (2) a semi-automated method for cell tracking that works up to the 8-cell stage, along with a software implementation available to the public (this software was used to build the reported database); (3) an algorithm for automatic tracking up to the 4-cell stage, based on histograms of mirror symmetry coefficients captured using wavelets; (4) a cell-tracking database containing 100 annotated examples of mammalian embryos up to the 8-cell sta≥ and (5) statistical analysis of various timing distributions obtained from those examples.

Original languageEnglish (US)
Pages (from-to)24-34
Number of pages11
JournalComputers in Biology and Medicine
Volume51
DOIs
StatePublished - Aug 1 2014

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Keywords

  • Cell counting
  • Database
  • Dynamic programming
  • Embryo development
  • Event detection
  • Time series
  • Tracking

ASJC Scopus subject areas

  • Computer Science Applications
  • Health Informatics

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