By Topic

Automated lineage tree reconstruction from Caenorhabditis elegans image data using particle filtering based cell tracking

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

5 Author(s)
Noemí Carranza ; Biomedical Imaging Group Rotterdam, Departments of Medical Informatics and Radiology, Erasmus MC - University Medical Center Rotterdam, The Netherlands ; Ihor Smal ; Oleh Dzyubachyk ; Wiro Niessen
more authors

Caenorhabditis elegans is an important model organism for the study of molecular mechanisms of development and disease processes, due to its well-known genome and invariant cell lineage tree. Such studies generally produce vast amounts of image data, and require very robust and efficient algorithms to extract and characterize lineage phenotypes and to determine gene expression patterns. Previously published methods for this purpose show only mediocre performance and often require extensive manual post-processing. The challenge remains to develop more powerful and fully automated methods. In this paper we propose a new algorithm for C. elegans cell tracking and lineage reconstruction, based on a Bayesian estimation framework, implemented by means of particle filtering. The tracking is enhanced with a detection stage, based on the h-dome transform. Preliminary experiments on several image sequences demonstrate that the new tracking algorithm is able to reconstruct the lineage tree, at least until the 350-cell stage, without manual intervention, at low computational cost and with low error rates.

Published in:

2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro

Date of Conference:

March 30 2011-April 2 2011