Morphology-Guided Graph Search for Untangling Objects: C. elegans Analysis

We present a novel approach for extracting cluttered objects based on their morphological properties1 . Specifically, we address the problem of untangling C. 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 overlaps. 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 ratio.

• Type: Journal
• Year: 2010
• Where: MICCAI
• Authors: Tammy Riklin Raviv, Vebjorn Ljosa, Annie L. Conery, Frederick M. Ausubel, Anne E. Carpenter, Polina Golland, Carolina Wählby
• Keywords: Medical Imaging, MICCAI 2010, Sparse Directed Graph, Worm, Worm Detection Ratio