Morphology-guided graph search for untangling objects: C. elegans analysis

T Riklin Raviv, Vebjorn Ljosa, Annie L Conery, Frederick M Ausubel, Anne E Carpenter, Polina Golland, Carolina Wählby

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

18 Scopus citations

Abstract

We present a novel approach for extracting cluttered objects based on their morphological properties. Specifically, we address the problem of untangling Caenorhabditis elegans clusters in high-throughput screening experiments. We represent the skeleton of each worm cluster by a sparse directed graph whose vertices and edges correspond to worm segments and their adjacencies, respectively. We then search for paths in the graph that are most likely to represent worms while minimizing overlap. The worm likelihood measure is defined on a low-dimensional feature space that captures different worm poses, obtained from a training set of isolated worms. We test the algorithm on 236 microscopy images, each containing 15 C. elegans worms, and demonstrate successful cluster untangling and high worm detection accuracy.
Original languageEnglish
Title of host publicationInternational Conference on Medical Image Computing and Computer-Assisted Intervention
PublisherSpringer Berlin Heidelberg
Pages634-641
Number of pages8
ISBN (Electronic)978-3-642-15711-0
ISBN (Print)978-3-642-15710-3
DOIs
StatePublished - 2010

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