Morphology-Guided Graph Search for Untangling Objects: C. elegans Analysis
(0) 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.
Tammy Riklin Raviv V.Ljosa A.L.Conery F.M.Ausubel A.E.Carpenter P.Golland C.Wahlby
Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA Imaging Platform, Broad Institute of MIT and Harvard, Cambridge, MA Dept.of Molecular Biology and Center for Computational and Integrative Biology,Mass.General Hospital
国际会议
北京
英文
634–641
2010-09-01（万方平台首次上网日期，不代表论文的发表时间）