TY - GEN
T1 - Morphology-guided graph search for untangling objects
T2 - 13th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2010
AU - Raviv, T. Riklin
AU - Ljosa, V.
AU - Conery, A. L.
AU - Ausubel, F. M.
AU - Carpenter, A. E.
AU - Golland, P.
AU - Wählby, C.
PY - 2010/11/22
Y1 - 2010/11/22
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84858779940&partnerID=8YFLogxK
U2 - 10.1007/978-3-642-15711-0_79
DO - 10.1007/978-3-642-15711-0_79
M3 - Conference contribution
C2 - 20879454
AN - SCOPUS:84858779940
SN - 3642157106
SN - 9783642157103
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 634
EP - 641
BT - Medical Image Computing and Computer-Assisted Intervention, MICCAI2010 - 13th International Conference, Proceedings
Y2 - 20 September 2010 through 24 September 2010
ER -