Morphology-Guided Graph Search for Untangling

Abstract: In the talk I will present a computational approach for extracting cluttered objects based on their morphological properties. Specifically, the problem of untangling Caenorhabditis elegans clusters in high-throughput microscopy images, is addressed. 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 tested the algorithm on microscopy images, each containing C. elegans worms, and demonstrate successful cluster untangling and high worm detection accuracy. Bio: Dr. Tammy Riklin Raviv is a faculty member at the Electrical and Computer Engineering Department of Ben-Gurion University since November 2012.Her research focuses on the on the development of mathematical and algorithmic tools for processing, analysis and understanding of natural, biological and medical images. She holds a B.Sc. in Physics and a M.Sc. in Computer Science from the Hebrew University of Jerusalem, Israel. She received her Ph.D. from the School of Electrical Engineering of Tel-Aviv University. In the years 2008-2013 she was a post-doctoral associate at the Computer Science and Artificial Intelligence Laboratory (CSAIL) at the Massachusetts Institute of Technology (MIT) and a research fellow at Harvard Medical School and at the Broad Institute of MIT and Harvard.