title: Morphology-guided graph search for untangling objects: C. elegans analysis.
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.
authors.affiliation: Computer Science and Artificial Intelligence Laboratory, MIT, Cambridge, MA, USA.
authors.forename: Tammy Riklin
authors.initials: TR
authors.lastname: Raviv
authors.forename: V
authors.initials: V
authors.lastname: Ljosa
authors.forename: A L
authors.initials: AL
authors.lastname: Conery
authors.forename: F M
authors.initials: FM
authors.lastname: Ausubel
authors.forename: A E
authors.initials: AE
authors.lastname: Carpenter
authors.forename: P
authors.initials: P
authors.lastname: Golland
authors.forename: C
authors.initials: C
authors.lastname: Wählby
mesh_terms: D000465:Algorithms; D000818:Animals; D017173:Caenorhabditis elegans / Q000166:cytology*; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008853:Microscopy / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity