Shape statistics for cell division detection in time-lapse videos of early mouse embryo

M. Cicconet, K. Gunsalus, D. Geiger, M. Werman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We describe a statistical approach to the problem of estimating the times of cell-division cycles in time-lapse movies of early mouse embryos. Our method is based on the likelihoods for cells of certain radii ranges to be in each frame - without actually locating or counting the cells. Computing the likelihoods consists of a voting scheme where votes come form quadruples of points in a way similar to the first step of the Randomized Hough Transform for ellipse detection. To locate divisions, we search for points of abrupt change in the matrix of likelihoods (built for all frames), and pick the two optimal division points using a dynamic programming algorithm. Our results for the first and second cell division cycles differ less than two frames from the medians of the annotated times in a database of 100 annotated videos, and outperform two other recent methods in the same set.

Original languageEnglish (US)
Title of host publication2014 IEEE International Conference on Image Processing, ICIP 2014
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3622-3625
Number of pages4
ISBN (Electronic)9781479957514
DOIs
StatePublished - Jan 28 2014

Publication series

Name2014 IEEE International Conference on Image Processing, ICIP 2014

Keywords

  • division detection
  • mouse embryo
  • shape statistics
  • time lapse
  • video analysis

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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